[7.2.0] Docs: removal of migrated deep learning frameworks (#6098 )

docs: add details to xdit history page (#6040 )
(cherry picked from commit 771f5f28eb)
2026-03-31 14:37:56 -04:00 · 2026-03-17 12:36:38 -04:00 · 2026-03-17 09:53:47 -04:00 · 2026-03-02 11:51:34 -05:00 · 2026-02-25 16:41:43 -05:00 · 2026-02-24 11:08:26 -05:00
137 changed files with 26909 additions and 4131 deletions
@@ -128,6 +128,9 @@ jobs:
      parameters:
        aptPackages: ${{ parameters.aptPackages }}
        pipModules: ${{ parameters.pipModules }}
+    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-cmake-custom.yml
+      parameters:
+        cmakeVersion: '3.28.6'
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
      parameters:
@@ -152,6 +155,7 @@ jobs:
          -DCMAKE_BUILD_TYPE=Release
          -DGPU_TARGETS=${{ job.target }}
          -DAMDGPU_TARGETS=${{ job.target }}
+          -DCMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang++
          -DCMAKE_MODULE_PATH=$(Agent.BuildDirectory)/rocm/lib/cmake/hip
          -DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm/llvm;$(Agent.BuildDirectory)/rocm
          -DHALF_INCLUDE_DIR=$(Agent.BuildDirectory)/rocm/include
@@ -192,6 +196,9 @@ jobs:
      parameters:
        aptPackages: ${{ parameters.aptPackages }}
        pipModules: ${{ parameters.pipModules }}
+    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-cmake-custom.yml
+      parameters:
+        cmakeVersion: '3.28.6'
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
      parameters:
@@ -217,6 +224,7 @@ jobs:
          -DCMAKE_BUILD_TYPE=Release
          -DGPU_TARGETS=${{ job.target }}
          -DAMDGPU_TARGETS=${{ job.target }}
+          -DCMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang++
          -DCMAKE_MODULE_PATH=$(Agent.BuildDirectory)/rocm/lib/cmake/hip
          -DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm/llvm;$(Agent.BuildDirectory)/rocm
          -DHALF_INCLUDE_DIR=$(Agent.BuildDirectory)/rocm/include
@@ -34,6 +34,7 @@ parameters:
  default:
    - cmake
    - libnuma-dev
+    - libsimde-dev
    - mesa-common-dev
    - ninja-build
    - ocl-icd-libopencl1
@@ -1,10 +1,29 @@
 parameters:
+- name: componentName
+  type: string
+  default: amdsmi
 - name: checkoutRepo
  type: string
  default: 'self'
 - name: checkoutRef
  type: string
  default: ''
+# monorepo related parameters
+- name: sparseCheckoutDir
+  type: string
+  default: ''
+- name: triggerDownstreamJobs
+  type: boolean
+  default: false
+- name: downstreamAggregateNames
+  type: string
+  default: ''
+- name: buildDependsOn
+  type: object
+  default: null
+- name: unifiedBuild
+  type: boolean
+  default: false
 # set to true if doing full build of ROCm stack
 # and dependencies are pulled from same pipeline
 - name: aggregatePipeline
@@ -31,7 +50,7 @@ parameters:

 jobs:
 - ${{ each job in parameters.jobMatrix.buildJobs }}:
-  - job: amdsmi_build_${{ job.os }}
+  - job: ${{ parameters.componentName }}_build_${{ job.os }}
    pool:
      ${{ if eq(job.os, 'ubuntu2404') }}:
        vmImage: 'ubuntu-24.04'
@@ -55,6 +74,7 @@ jobs:
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
      parameters:
        checkoutRepo: ${{ parameters.checkoutRepo }}
+        sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
      parameters:
        os: ${{ job.os }}
@@ -65,50 +85,54 @@ jobs:
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
      parameters:
        os: ${{ job.os }}
+        componentName: ${{ parameters.componentName }}
+        sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
      parameters:
        os: ${{ job.os }}
+        componentName: ${{ parameters.componentName }}
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
    # - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
    #   parameters:
    #     aptPackages: ${{ parameters.aptPackages }}

- ${{ each job in parameters.jobMatrix.testJobs }}:
-  - job: amdsmi_test_${{ job.os }}_${{ job.target }}
-    dependsOn: amdsmi_build_${{ job.os }}
-    condition:
-      and(succeeded(),
-        eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
-        not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), variables['Build.DefinitionName'])),
-        eq(${{ parameters.aggregatePipeline }}, False)
-      )
-    variables:
-    - group: common
-    - template: /.azuredevops/variables-global.yml
-    pool: ${{ job.target }}_test_pool
-    workspace:
-      clean: all
-    steps:
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
-      parameters:
-        aptPackages: ${{ parameters.aptPackages }}
-        packageManager: ${{ job.packageManager }}
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
-      parameters:
-        os: ${{ job.os }}
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
-      parameters:
-        runRocminfo: false
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
-      parameters:
-        componentName: amdsmi
-        testDir: '$(Agent.BuildDirectory)'
-        testExecutable: 'sudo ./rocm/share/amd_smi/tests/amdsmitst'
-        testParameters: '--gtest_output=xml:./test_output.xml --gtest_color=yes'
-        os: ${{ job.os }}
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
-      parameters:
-        aptPackages: ${{ parameters.aptPackages }}
-        environment: test
-        gpuTarget: ${{ job.target }}
+- ${{ if eq(parameters.unifiedBuild, False) }}:
+  - ${{ each job in parameters.jobMatrix.testJobs }}:
+    - job: ${{ parameters.componentName }}_test_${{ job.os }}_${{ job.target }}
+      dependsOn: ${{ parameters.componentName }}_build_${{ job.os }}
+      condition:
+        and(succeeded(),
+          eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
+          not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), '${{ parameters.componentName }}')),
+          eq(${{ parameters.aggregatePipeline }}, False)
+        )
+      variables:
+      - group: common
+      - template: /.azuredevops/variables-global.yml
+      pool: ${{ job.target }}_test_pool
+      workspace:
+        clean: all
+      steps:
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
+        parameters:
+          aptPackages: ${{ parameters.aptPackages }}
+          packageManager: ${{ job.packageManager }}
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
+        parameters:
+          os: ${{ job.os }}
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
+        parameters:
+          runRocminfo: false
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
+        parameters:
+          componentName: ${{ parameters.componentName }}
+          testDir: '$(Agent.BuildDirectory)'
+          testExecutable: 'sudo ./rocm/share/amd_smi/tests/amdsmitst'
+          testParameters: '--gtest_output=xml:./test_output.xml --gtest_color=yes'
+          os: ${{ job.os }}
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
+        parameters:
+          aptPackages: ${{ parameters.aptPackages }}
+          environment: test
+          gpuTarget: ${{ job.target }}
@@ -32,7 +32,6 @@ parameters:
 - name: aptPackages
  type: object
  default:
-    - cmake
    - gfortran
    - git
    - libboost-program-options-dev
@@ -42,6 +41,7 @@ parameters:
 - name: rocmDependencies
  type: object
  default:
+    - aomp
    - clr
    - llvm-project
    - rocminfo
@@ -51,6 +51,7 @@ parameters:
 - name: rocmTestDependencies
  type: object
  default:
+    - aomp
    - clr
    - llvm-project
    - hipBLAS-common
@@ -103,6 +104,7 @@ jobs:
      parameters:
        aptPackages: ${{ parameters.aptPackages }}
        packageManager: ${{ job.packageManager }}
+    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-cmake-custom.yml
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
      parameters:
@@ -128,6 +130,7 @@ jobs:
          -DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm;$(Agent.BuildDirectory)/vendor
          -DCMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang++
          -DCMAKE_C_COMPILER=$(Agent.BuildDirectory)/rocm/bin/amdclang
+          -DCMAKE_Fortran_COMPILER=gfortran
          -DCMAKE_BUILD_TYPE=Release
          -DBUILD_CLIENTS_TESTS=ON
          -DBUILD_CLIENTS_SAMPLES=OFF
@@ -60,6 +60,7 @@ parameters:
    - rocprofiler-register
    - ROCR-Runtime
    - roctracer
+    - rocSPARSE
 - name: rocmTestDependencies
  type: object
  default:
@@ -74,6 +75,7 @@ parameters:
    - rocprofiler-register
    - ROCR-Runtime
    - roctracer
+    - rocSPARSE

 - name: jobMatrix
  type: object
@@ -1,10 +1,29 @@
 parameters:
+- name: componentName
+  type: string
+  default: hipTensor
 - name: checkoutRepo
  type: string
  default: 'self'
 - name: checkoutRef
  type: string
  default: ''
+# monorepo related parameters
+- name: sparseCheckoutDir
+  type: string
+  default: ''
+- name: triggerDownstreamJobs
+  type: boolean
+  default: false
+- name: downstreamAggregateNames
+  type: string
+  default: ''
+- name: buildDependsOn
+  type: object
+  default: null
+- name: unifiedBuild
+  type: boolean
+  default: false
 # set to true if doing full build of ROCm stack
 # and dependencies are pulled from same pipeline
 - name: aggregatePipeline
@@ -51,7 +70,7 @@ parameters:

 jobs:
 - ${{ each job in parameters.jobMatrix.buildJobs }}:
-  - job: hipTensor_build_${{ job.target }}
+  - job: ${{ parameters.componentName }}_build_${{ job.target }}
    variables:
    - group: common
    - template: /.azuredevops/variables-global.yml
@@ -66,12 +85,15 @@ jobs:
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
      parameters:
        checkoutRepo: ${{ parameters.checkoutRepo }}
+        sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
      parameters:
        checkoutRef: ${{ parameters.checkoutRef }}
        dependencyList: ${{ parameters.rocmDependencies }}
        gpuTarget: ${{ job.target }}
        aggregatePipeline: ${{ parameters.aggregatePipeline }}
+        ${{ if parameters.triggerDownstreamJobs }}:
+          downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
      parameters:
        extraBuildFlags: >-
@@ -85,9 +107,12 @@ jobs:
          -GNinja
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
      parameters:
+        componentName: ${{ parameters.componentName }}
+        sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
        gpuTarget: ${{ job.target }}
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
      parameters:
+        componentName: ${{ parameters.componentName }}
        gpuTarget: ${{ job.target }}
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
@@ -95,44 +120,47 @@ jobs:
        aptPackages: ${{ parameters.aptPackages }}
        gpuTarget: ${{ job.target }}

- ${{ each job in parameters.jobMatrix.testJobs }}:
-  - job: hipTensor_test_${{ job.target }}
-    timeoutInMinutes: 90
-    dependsOn: hipTensor_build_${{ job.target }}
-    condition:
-      and(succeeded(),
-        eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
-        not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), variables['Build.DefinitionName'])),
-        eq(${{ parameters.aggregatePipeline }}, False)
-      )
-    variables:
-    - group: common
-    - template: /.azuredevops/variables-global.yml
-    pool: ${{ job.target }}_test_pool
-    workspace:
-      clean: all
-    steps:
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
-      parameters:
-        aptPackages: ${{ parameters.aptPackages }}
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
-      parameters:
-        gpuTarget: ${{ job.target }}
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aqlprofile.yml
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
-      parameters:
-        checkoutRef: ${{ parameters.checkoutRef }}
-        dependencyList: ${{ parameters.rocmTestDependencies }}
-        gpuTarget: ${{ job.target }}
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
-      parameters:
-        componentName: hipTensor
-        testDir: '$(Agent.BuildDirectory)/rocm/bin/hiptensor'
-        testParameters: '-E ".*-extended" --output-on-failure --force-new-ctest-process --output-junit test_output.xml'
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
-      parameters:
-        aptPackages: ${{ parameters.aptPackages }}
-        environment: test
-        gpuTarget: ${{ job.target }}
+- ${{ if eq(parameters.unifiedBuild, False) }}:
+  - ${{ each job in parameters.jobMatrix.testJobs }}:
+    - job: ${{ parameters.componentName }}_test_${{ job.target }}
+      timeoutInMinutes: 90
+      dependsOn: ${{ parameters.componentName }}_build_${{ job.target }}
+      condition:
+        and(succeeded(),
+          eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
+          not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), '${{ parameters.componentName }}')),
+          eq(${{ parameters.aggregatePipeline }}, False)
+        )
+      variables:
+      - group: common
+      - template: /.azuredevops/variables-global.yml
+      pool: ${{ job.target }}_test_pool
+      workspace:
+        clean: all
+      steps:
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
+        parameters:
+          aptPackages: ${{ parameters.aptPackages }}
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
+        parameters:
+          gpuTarget: ${{ job.target }}
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aqlprofile.yml
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
+        parameters:
+          checkoutRef: ${{ parameters.checkoutRef }}
+          dependencyList: ${{ parameters.rocmTestDependencies }}
+          gpuTarget: ${{ job.target }}
+          ${{ if parameters.triggerDownstreamJobs }}:
+            downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
+        parameters:
+          componentName: ${{ parameters.componentName }}
+          testDir: '$(Agent.BuildDirectory)/rocm/bin/hiptensor'
+          testParameters: '-E ".*-extended" --extra-verbose --output-on-failure --force-new-ctest-process --output-junit test_output.xml'
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
+        parameters:
+          aptPackages: ${{ parameters.aptPackages }}
+          environment: test
+          gpuTarget: ${{ job.target }}
@@ -39,6 +39,7 @@ parameters:
    - python3
    - python3-dev
    - python3-pip
+    - python3-venv
    - libgtest-dev
    - libboost-filesystem-dev
    - libboost-program-options-dev
@@ -46,6 +47,8 @@ parameters:
  type: object
  default:
    - nanobind>=2.0.0
+    - pytest
+    - pytest-cov
 - name: rocmDependencies
  type: object
  default:
@@ -72,8 +75,10 @@ parameters:
      - { os: ubuntu2204, packageManager: apt }
      - { os: almalinux8, packageManager: dnf }
    testJobs:
-      - { os: ubuntu2204, packageManager: apt, target: gfx942 }
      - { os: ubuntu2204, packageManager: apt, target: gfx90a }
+      # - { os: ubuntu2204, packageManager: apt, target: gfx1100 }
+      # - { os: ubuntu2204, packageManager: apt, target: gfx1151 }
+      # - { os: ubuntu2204, packageManager: apt, target: gfx1201 }
 - name: downstreamComponentMatrix
  type: object
  default:
@@ -116,6 +121,11 @@ jobs:
      parameters:
        dependencyList:
          - gtest
+    - ${{ if ne(job.os, 'almalinux8') }}:
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-vendor.yml
+        parameters:
+          dependencyList:
+            - catch2
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
      parameters:
        checkoutRepo: ${{ parameters.checkoutRepo }}
@@ -137,6 +147,7 @@ jobs:
          -DORIGAMI_BUILD_SHARED_LIBS=ON
          -DORIGAMI_ENABLE_PYTHON=ON
          -DORIGAMI_BUILD_TESTING=ON
+          -DORIGAMI_ENABLE_FETCH=ON
          -GNinja
    - ${{ if ne(job.os, 'almalinux8') }}:
      - task: PublishPipelineArtifact@1
@@ -169,7 +180,6 @@ jobs:
      dependsOn: origami_build_${{ job.os }}
      condition:
        and(succeeded(),
-          eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
          not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), '${{ parameters.componentName }}')),
          eq(${{ parameters.aggregatePipeline }}, False)
        )
@@ -180,30 +190,30 @@ jobs:
      workspace:
        clean: all
      steps:
-      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
-        parameters:
-          checkoutRepo: ${{ parameters.checkoutRepo }}
-          sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
        parameters:
          aptPackages: ${{ parameters.aptPackages }}
          pipModules: ${{ parameters.pipModules }}
          packageManager: ${{ job.packageManager }}
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-cmake-custom.yml
      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
+        parameters:
+          checkoutRepo: ${{ parameters.checkoutRepo }}
+          sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-vendor.yml
+        parameters:
+          dependencyList:
+            - gtest
+      - ${{ if ne(job.os, 'almalinux8') }}:
+        - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-vendor.yml
+          parameters:
+            dependencyList:
+              - catch2
      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
        parameters:
          preTargetFilter: ${{ parameters.componentName }}
          os: ${{ job.os }}
-      - task: DownloadPipelineArtifact@2
-        displayName: 'Download Build Directory Artifact'
-        inputs:
-          artifact: '${{ parameters.componentName }}_${{ job.os }}_build_dir'
-          path: '$(Agent.BuildDirectory)/s/build'
-      - task: DownloadPipelineArtifact@2
-        displayName: 'Download Python Source Artifact'
-        inputs:
-          artifact: '${{ parameters.componentName }}_${{ job.os }}_python_src'
-          path: '$(Agent.BuildDirectory)/s/python'
      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
        parameters:
          checkoutRef: ${{ parameters.checkoutRef }}
@@ -212,25 +222,72 @@ jobs:
          gpuTarget: ${{ job.target }}
          ${{ if parameters.triggerDownstreamJobs }}:
            downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
+      - task: CMake@1
+        displayName: 'Origami Test CMake Configuration'
+        inputs:
+          cmakeArgs: >-
+            -DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm;$(Agent.BuildDirectory)/vendor
+            -DCMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang++
+            -DORIGAMI_BUILD_SHARED_LIBS=ON
+            -DORIGAMI_ENABLE_PYTHON=ON
+            -DORIGAMI_BUILD_TESTING=ON
+            -GNinja
+            $(Agent.BuildDirectory)/s
+      - task: Bash@3
+        displayName: 'Build Origami Tests and Python Bindings'
+        inputs:
+          targetType: inline
+          workingDirectory: build
+          script: |
+            cmake --build . --target origami-tests origami_python -- -j$(nproc)
      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
+      # Run tests using CTest (discovers and runs both C++ and Python tests)
      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
        parameters:
          componentName: ${{ parameters.componentName }}
          os: ${{ job.os }}
-          testDir: '$(Agent.BuildDirectory)/rocm/bin'
-          testExecutable: './origami-tests'
-          testParameters: '--yaml origami-tests.yaml --gtest_output=xml:./test_output.xml --gtest_color=yes'
-      - script: |
-          set -e
-          export PYTHONPATH=$(Agent.BuildDirectory)/s/build/python:$PYTHONPATH
-
-          echo "--- Running origami_test.py ---"
-          python3 $(Agent.BuildDirectory)/s/python/origami_test.py
-          
-          echo "--- Running origami_grid_test.py ---"
-          python3 $(Agent.BuildDirectory)/s/python/origami_grid_test.py
-        displayName: 'Run Python Binding Tests'
-        condition: succeeded()
+          testDir: 'build'
+          testParameters: '--output-on-failure --force-new-ctest-process --output-junit test_output.xml'
+      # Test pip install workflow
+      # - task: Bash@3
+      #   displayName: 'Test Pip Install'
+      #   inputs:
+      #     targetType: inline
+      #     script: |
+      #       set -e
+            
+      #       echo "==================================================================="
+      #       echo "Testing pip install workflow (pip install -e .)"
+      #       echo "==================================================================="
+            
+      #       # Set environment variables for pip install CMake build
+      #       export ROCM_PATH=$(Agent.BuildDirectory)/rocm
+      #       export CMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm:$(Agent.BuildDirectory)/vendor
+      #       export CMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang++
+            
+      #       echo "ROCM_PATH: $ROCM_PATH"
+      #       echo "CMAKE_PREFIX_PATH: $CMAKE_PREFIX_PATH"
+      #       echo "CMAKE_CXX_COMPILER: $CMAKE_CXX_COMPILER"
+      #       echo ""
+            
+      #       # Install from source directory
+      #       cd "$(Agent.BuildDirectory)/s/python"
+      #       pip install -e .
+            
+      #       # Verify import works
+      #       echo ""
+      #       echo "Verifying origami can be imported..."
+      #       python3 -c "import origami; print('✓ Successfully imported origami')"
+            
+      #       # Run pytest on installed package
+      #       echo ""
+      #       echo "Running pytest tests..."
+      #       python3 -m pytest tests/ -v -m "not slow" --tb=short
+            
+      #       echo ""
+      #       echo "==================================================================="
+      #       echo "Pip install test completed successfully"
+      #       echo "==================================================================="
      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
        parameters:
          aptPackages: ${{ parameters.aptPackages }}
@@ -1,10 +1,35 @@
 parameters:
+- name: componentName
+  type: string
+  default: rccl
 - name: checkoutRepo
  type: string
  default: 'self'
 - name: checkoutRef
  type: string
  default: ''
+- name: systemsRepo
+  type: string
+  default: systems_repo
+- name: systemsSparseCheckoutDir
+  type: string
+  default: 'projects/rocprofiler-sdk'
+# monorepo related parameters
+- name: sparseCheckoutDir
+  type: string
+  default: ''
+- name: triggerDownstreamJobs
+  type: boolean
+  default: false
+- name: downstreamAggregateNames
+  type: string
+  default: ''
+- name: buildDependsOn
+  type: object
+  default: null
+- name: unifiedBuild
+  type: boolean
+  default: false
 # set to true if doing full build of ROCm stack
 # and dependencies are pulled from same pipeline
 - name: aggregatePipeline
@@ -57,19 +82,28 @@ parameters:
  type: object
  default:
    buildJobs:
-      - gfx942:
-        target: gfx942
-      - gfx90a:
-        target: gfx90a
+      - { os: ubuntu2204, packageManager: apt, target: gfx942 }
+      - { os: ubuntu2204, packageManager: apt, target: gfx90a }
    testJobs:
-      - gfx942:
-        target: gfx942
-      - gfx90a:
-        target: gfx90a
+      - { os: ubuntu2204, packageManager: apt, target: gfx942 }
+      - { os: ubuntu2204, packageManager: apt, target: gfx90a }
+- name: downstreamComponentMatrix
+  type: object
+  default:
+    - rocprofiler-sdk:
+      name: rocprofiler-sdk
+      sparseCheckoutDir: ''
+      skipUnifiedBuild: 'false'
+      buildDependsOn:
+        - rccl_build

 jobs:
 - ${{ each job in parameters.jobMatrix.buildJobs }}:
-  - job: rccl_build_${{ job.target }}
+  - job: ${{ parameters.componentName }}_build_${{ job.os }}_${{ job.target }}
+    ${{ if parameters.buildDependsOn }}:
+      dependsOn:
+        - ${{ each build in parameters.buildDependsOn }}:
+          - ${{ build }}_${{ job.os }}_${{ job.target }}
    timeoutInMinutes: 120
    variables:
    - group: common
@@ -77,17 +111,23 @@ jobs:
    - name: HIP_ROCCLR_HOME
      value: $(Build.BinariesDirectory)/rocm
    pool: ${{ variables.MEDIUM_BUILD_POOL }}
+    ${{ if eq(job.os, 'almalinux8') }}:
+      container:
+        image: rocmexternalcicd.azurecr.io/manylinux228:latest
+        endpoint: ContainerService3
    workspace:
      clean: all
    steps:
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
      parameters:
        aptPackages: ${{ parameters.aptPackages }}
+        packageManager: ${{ job.packageManager }}
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-cmake-custom.yml
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
      parameters:
        checkoutRepo: ${{ parameters.checkoutRepo }}
+        sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
        submoduleBehaviour: recursive
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-vendor.yml
      parameters:
@@ -97,10 +137,14 @@ jobs:
      parameters:
        checkoutRef: ${{ parameters.checkoutRef }}
        dependencyList: ${{ parameters.rocmDependencies }}
+        os: ${{ job.os }}
        gpuTarget: ${{ job.target }}
        aggregatePipeline: ${{ parameters.aggregatePipeline }}
+        ${{ if parameters.triggerDownstreamJobs }}:
+            downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
      parameters:
+        os: ${{ job.os }}
        extraBuildFlags: >-
          -DCMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/bin/hipcc
          -DCMAKE_C_COMPILER=$(Agent.BuildDirectory)/rocm/bin/hipcc
@@ -112,58 +156,87 @@ jobs:
          -GNinja
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
      parameters:
+        componentName: ${{ parameters.componentName }}
+        sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
+        os: ${{ job.os }}
        gpuTarget: ${{ job.target }}
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
      parameters:
+        componentName: ${{ parameters.componentName }}
+        os: ${{ job.os }}
        gpuTarget: ${{ job.target }}
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
-      parameters:
-        aptPackages: ${{ parameters.aptPackages }}
-        gpuTarget: ${{ job.target }}
-        extraEnvVars:
-          - HIP_ROCCLR_HOME:::/home/user/workspace/rocm
-        installLatestCMake: true
+    - ${{ if eq(job.os, 'ubuntu2204') }}:
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
+        parameters:
+          aptPackages: ${{ parameters.aptPackages }}
+          gpuTarget: ${{ job.target }}
+          extraEnvVars:
+            - HIP_ROCCLR_HOME:::/home/user/workspace/rocm
+          installLatestCMake: true

- ${{ each job in parameters.jobMatrix.testJobs }}:
-  - job: rccl_test_${{ job.target }}
-    timeoutInMinutes: 120
-    dependsOn: rccl_build_${{ job.target }}
-    condition:
-      and(succeeded(),
-        eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
-        not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), variables['Build.DefinitionName'])),
-        eq(${{ parameters.aggregatePipeline }}, False)
-      )
-    variables:
-    - group: common
-    - template: /.azuredevops/variables-global.yml
-    pool: ${{ job.target }}_test_pool
-    workspace:
-      clean: all
-    steps:
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
-      parameters:
-        aptPackages: ${{ parameters.aptPackages }}
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
-      parameters:
-        gpuTarget: ${{ job.target }}
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aqlprofile.yml
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
-      parameters:
-        checkoutRef: ${{ parameters.checkoutRef }}
-        dependencyList: ${{ parameters.rocmTestDependencies }}
-        gpuTarget: ${{ job.target }}
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
-      parameters:
-        componentName: rccl
-        testDir: '$(Agent.BuildDirectory)/rocm/bin'
-        testExecutable: './rccl-UnitTests'
-        testParameters: '--gtest_output=xml:./test_output.xml --gtest_color=yes'
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
-      parameters:
-        aptPackages: ${{ parameters.aptPackages }}
-        environment: test
-        gpuTarget: ${{ job.target }}
+- ${{ if eq(parameters.unifiedBuild, False) }}:
+  - ${{ each job in parameters.jobMatrix.testJobs }}:
+    - job: ${{ parameters.componentName }}_test_${{ job.os }}_${{ job.target }}
+      timeoutInMinutes: 120
+      dependsOn: ${{ parameters.componentName }}_build_${{ job.os }}_${{ job.target }}
+      condition:
+        and(succeeded(),
+          eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
+          not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), '${{ parameters.componentName }}')),
+          eq(${{ parameters.aggregatePipeline }}, False)
+        )
+      variables:
+      - group: common
+      - template: /.azuredevops/variables-global.yml
+      pool: ${{ job.target }}_test_pool
+      workspace:
+        clean: all
+      steps:
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
+        parameters:
+          aptPackages: ${{ parameters.aptPackages }}
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
+        parameters:
+          preTargetFilter: ${{ parameters.componentName }}
+          os: ${{ job.os }}
+          gpuTarget: ${{ job.target }}
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aqlprofile.yml
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
+        parameters:
+          checkoutRef: ${{ parameters.checkoutRef }}
+          dependencyList: ${{ parameters.rocmTestDependencies }}
+          os: ${{ job.os }}
+          gpuTarget: ${{ job.target }}
+          ${{ if parameters.triggerDownstreamJobs }}:
+            downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
+        parameters:
+          componentName: ${{ parameters.componentName }}
+          os: ${{ job.os }}
+          testDir: '$(Agent.BuildDirectory)/rocm/bin'
+          testExecutable: './rccl-UnitTests'
+          testParameters: '--gtest_output=xml:./test_output.xml --gtest_color=yes'
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
+        parameters:
+          aptPackages: ${{ parameters.aptPackages }}
+          environment: test
+          gpuTarget: ${{ job.target }}
+
+- ${{ if parameters.triggerDownstreamJobs }}:
+  - ${{ each component in parameters.downstreamComponentMatrix }}:
+    - ${{ if not(and(parameters.unifiedBuild, eq(component.skipUnifiedBuild, 'true'))) }}:
+      - template: /.azuredevops/components/${{ component.name }}.yml@pipelines_repo
+        parameters:
+          checkoutRepo: ${{ parameters.systemsRepo }}
+          sparseCheckoutDir: ${{ parameters.systemsSparseCheckoutDir }}
+          triggerDownstreamJobs: true
+          unifiedBuild: ${{ parameters.unifiedBuild }}
+          ${{ if parameters.unifiedBuild }}:
+            buildDependsOn: ${{ component.unifiedBuild.buildDependsOn }}
+            downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}+${{ component.unifiedBuild.downstreamAggregateNames }}
+          ${{ else }}:
+            buildDependsOn: ${{ component.buildDependsOn }}
+            downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}+${{ parameters.componentName }}
@@ -1,10 +1,29 @@
 parameters:
+- name: componentName
+  type: string
+  default: rocWMMA
 - name: checkoutRepo
  type: string
  default: 'self'
 - name: checkoutRef
  type: string
  default: ''
+# monorepo related parameters
+- name: sparseCheckoutDir
+  type: string
+  default: ''
+- name: triggerDownstreamJobs
+  type: boolean
+  default: false
+- name: downstreamAggregateNames
+  type: string
+  default: ''
+- name: buildDependsOn
+  type: object
+  default: null
+- name: unifiedBuild
+  type: boolean
+  default: false
 # set to true if doing full build of ROCm stack
 # and dependencies are pulled from same pipeline
 - name: aggregatePipeline
@@ -66,7 +85,11 @@ parameters:

 jobs:
 - ${{ each job in parameters.jobMatrix.buildJobs }}:
-  - job: rocWMMA_build_${{ job.target }}
+  - job: ${{ parameters.componentName }}_build_${{ job.target }}
+    ${{ if parameters.buildDependsOn }}:
+      dependsOn:
+        - ${{ each build in parameters.buildDependsOn }}:
+          - ${{ build }}_${{ job.target }}
    variables:
    - group: common
    - template: /.azuredevops/variables-global.yml
@@ -81,6 +104,7 @@ jobs:
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
      parameters:
        checkoutRepo: ${{ parameters.checkoutRepo }}
+        sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
      parameters:
        checkoutRef: ${{ parameters.checkoutRef }}
@@ -102,9 +126,12 @@ jobs:
  # gfx1030 not supported in documentation
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
      parameters:
+        componentName: ${{ parameters.componentName }}
+        sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
        gpuTarget: ${{ job.target }}
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
      parameters:
+        componentName: ${{ parameters.componentName }}
        gpuTarget: ${{ job.target }}
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
@@ -112,43 +139,45 @@ jobs:
        aptPackages: ${{ parameters.aptPackages }}
        gpuTarget: ${{ job.target }}

- ${{ each job in parameters.jobMatrix.testJobs }}:
-  - job: rocWMMA_test_${{ job.target }}
-    timeoutInMinutes: 270
-    dependsOn: rocWMMA_build_${{ job.target }}
-    condition:
-      and(succeeded(),
-        eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
-        not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), variables['Build.DefinitionName'])),
-        eq(${{ parameters.aggregatePipeline }}, False)
-      )
-    variables:
-    - group: common
-    - template: /.azuredevops/variables-global.yml
-    pool: ${{ job.target }}_test_pool
-    workspace:
-      clean: all
-    steps:
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
-      parameters:
-        aptPackages: ${{ parameters.aptPackages }}
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
-      parameters:
-        gpuTarget: ${{ job.target }}
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aqlprofile.yml
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
-      parameters:
-        checkoutRef: ${{ parameters.checkoutRef }}
-        dependencyList: ${{ parameters.rocmTestDependencies }}
-        gpuTarget: ${{ job.target }}
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
-      parameters:
-        componentName: rocWMMA
-        testDir: '$(Agent.BuildDirectory)/rocm/bin/rocwmma'
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
-      parameters:
-        aptPackages: ${{ parameters.aptPackages }}
-        environment: test
-        gpuTarget: ${{ job.target }}
+- ${{ if eq(parameters.unifiedBuild, False) }}:
+  - ${{ each job in parameters.jobMatrix.testJobs }}:
+    - job: ${{ parameters.componentName }}_test_${{ job.target }}
+      timeoutInMinutes: 350
+      dependsOn: ${{ parameters.componentName }}_build_${{ job.target }}
+      condition:
+        and(succeeded(),
+          eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
+          not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), '${{ parameters.componentName }}')),
+          eq(${{ parameters.aggregatePipeline }}, False)
+        )
+      variables:
+      - group: common
+      - template: /.azuredevops/variables-global.yml
+      pool: ${{ job.target }}_test_pool
+      workspace:
+        clean: all
+      steps:
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
+        parameters:
+          aptPackages: ${{ parameters.aptPackages }}
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
+        parameters:
+          preTargetFilter: ${{ parameters.componentName }}
+          gpuTarget: ${{ job.target }}
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aqlprofile.yml
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
+        parameters:
+          checkoutRef: ${{ parameters.checkoutRef }}
+          dependencyList: ${{ parameters.rocmTestDependencies }}
+          gpuTarget: ${{ job.target }}
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
+        parameters:
+          componentName: ${{ parameters.componentName }}
+          testDir: '$(Agent.BuildDirectory)/rocm/bin/rocwmma'
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
+        parameters:
+          aptPackages: ${{ parameters.aptPackages }}
+          environment: test
+          gpuTarget: ${{ job.target }}
@@ -81,7 +81,7 @@ jobs:
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
      parameters:
        componentName: rocm-cmake
-        testParameters: '-E "pass-version-parent" --output-on-failure --force-new-ctest-process --output-junit test_output.xml'
+        testParameters: '-E "pass-version-parent" --extra-verbose --output-on-failure --force-new-ctest-process --output-junit test_output.xml'
        os: ${{ job.os }}
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
      parameters:
@@ -17,21 +17,39 @@ parameters:
    - libdw-dev
    - libglfw3-dev
    - libmsgpack-dev
-    - libomp-dev
    - libopencv-dev
    - libtbb-dev
    - libtiff-dev
    - libva-amdgpu-dev
+    - libva2-amdgpu
+    - mesa-amdgpu-va-drivers
    - libavcodec-dev
    - libavformat-dev
    - libavutil-dev
    - ninja-build
    - python3-pip
+    - protobuf-compiler
+    - libprotoc-dev
+    - libopencv-dev
+- name: pipModules
+  type: object
+  default:
+    - future==1.0.0
+    - pytz==2022.1
+    - numpy==1.23
+    - google==3.0.0
+    - protobuf==3.12.4
+    - onnx==1.12.0
+    - nnef==1.0.7
 - name: rocmDependencies
  type: object
  default:
    - AMDMIGraphX
+    - aomp
+    - aomp-extras
    - clr
+    - half
+    - composable_kernel
    - hipBLAS
    - hipBLAS-common
    - hipBLASLt
@@ -45,6 +63,10 @@ parameters:
    - llvm-project
    - MIOpen
    - MIVisionX
+    - rocm_smi_lib
+    - rccl
+    - rocAL
+    - rocALUTION
    - rocBLAS
    - rocDecode
    - rocFFT
@@ -63,7 +85,11 @@ parameters:
  type: object
  default:
    - AMDMIGraphX
+    - aomp
+    - aomp-extras
    - clr
+    - half
+    - composable_kernel
    - hipBLAS
    - hipBLAS-common
    - hipBLASLt
@@ -77,6 +103,10 @@ parameters:
    - llvm-project
    - MIOpen
    - MIVisionX
+    - rocm_smi_lib
+    - rccl
+    - rocAL
+    - rocALUTION
    - rocBLAS
    - rocDecode
    - rocFFT
@@ -121,6 +151,7 @@ jobs:
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
      parameters:
        aptPackages: ${{ parameters.aptPackages }}
+        pipModules: ${{ parameters.pipModules }}
        registerROCmPackages: true
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-cmake-custom.yml
      parameters:
@@ -220,5 +251,6 @@ jobs:
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
      parameters:
        aptPackages: ${{ parameters.aptPackages }}
+        pipModules: ${{ parameters.pipModules }}
        environment: test
        gpuTarget: ${{ job.target }}
@@ -65,6 +65,13 @@ parameters:
    - pytest
    - pytest-cov
    - pytest-xdist
+- name: rocmDependencies
+  type: object
+  default:
+    - clr
+    - llvm-project
+    - ROCR-Runtime
+    - rocprofiler-sdk
 - name: rocmTestDependencies
  type: object
  default:
@@ -101,10 +108,12 @@ jobs:
    ${{ if parameters.buildDependsOn }}:
      dependsOn:
        - ${{ each build in parameters.buildDependsOn }}:
-          - ${{ build }}_${{ job.os }}_${{ job.target }}
+          - ${{ build }}_${{ job.target }}
    variables:
    - group: common
    - template: /.azuredevops/variables-global.yml
+    - name: ROCM_PATH
+      value: $(Agent.BuildDirectory)/rocm
    pool:
      vmImage: ${{ variables.BASE_BUILD_POOL }}
    workspace:
@@ -119,6 +128,14 @@ jobs:
      parameters:
        checkoutRepo: ${{ parameters.checkoutRepo }}
        sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
+    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
+      parameters:
+        checkoutRef: ${{ parameters.checkoutRef }}
+        dependencyList: ${{ parameters.rocmDependencies }}
+        gpuTarget: ${{ job.target }}
+        aggregatePipeline: ${{ parameters.aggregatePipeline }}
+        ${{ if parameters.triggerDownstreamJobs }}:
+            downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
      parameters:
        extraBuildFlags: >-
@@ -79,27 +79,27 @@ parameters:
  type: object
  default:
    buildJobs:
-      - gfx942:
-        target: gfx942
-      - gfx90a:
-        target: gfx90a
+      - { os: ubuntu2204, packageManager: apt, target: gfx942 }
+      - { os: ubuntu2204, packageManager: apt, target: gfx90a }
    testJobs:
-      - gfx942:
-        target: gfx942
-      - gfx90a:
-        target: gfx90a
+      - { os: ubuntu2204, packageManager: apt, target: gfx942 }
+      - { os: ubuntu2204, packageManager: apt, target: gfx90a }

 jobs:
 - ${{ each job in parameters.jobMatrix.buildJobs }}:
-  - job: rocprofiler_sdk_build_${{ job.target }}
+  - job: rocprofiler_sdk_build_${{ job.os }}_${{ job.target }}
    ${{ if parameters.buildDependsOn }}:
      dependsOn:
        - ${{ each build in parameters.buildDependsOn }}:
-          - ${{ build }}_${{ job.target }}
+          - ${{ build }}_${{ job.os}}_${{ job.target }}
    variables:
    - group: common
    - template: /.azuredevops/variables-global.yml
    pool: ${{ variables.MEDIUM_BUILD_POOL }}
+    ${{ if eq(job.os, 'almalinux8') }}:
+      container:
+        image: rocmexternalcicd.azurecr.io/manylinux228:latest
+        endpoint: ContainerService3
    workspace:
      clean: all
    steps:
@@ -107,6 +107,7 @@ jobs:
      parameters:
        aptPackages: ${{ parameters.aptPackages }}
        pipModules: ${{ parameters.pipModules }}
+        packageManager: ${{ job.packageManager }}
        registerROCmPackages: true
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
@@ -118,6 +119,7 @@ jobs:
      parameters:
        checkoutRef: ${{ parameters.checkoutRef }}
        dependencyList: ${{ parameters.rocmDependencies }}
+        os: ${{ job.os }}
        gpuTarget: ${{ job.target }}
        aggregatePipeline: ${{ parameters.aggregatePipeline }}
        ${{ if parameters.triggerDownstreamJobs }}:
@@ -132,6 +134,7 @@ jobs:
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
      parameters:
        componentName: ${{ parameters.componentName }}
+        os: ${{ job.os }}
        extraBuildFlags: >-
          -DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm
          -DROCPROFILER_BUILD_TESTS=ON
@@ -143,6 +146,7 @@ jobs:
      parameters:
        componentName: ${{ parameters.componentName }}
        sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
+        os: ${{ job.os }}
        gpuTarget: ${{ job.target }}
    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
      parameters:
@@ -158,8 +162,8 @@ jobs:

 - ${{ if eq(parameters.unifiedBuild, False) }}:
  - ${{ each job in parameters.jobMatrix.testJobs }}:
-    - job: rocprofiler_sdk_test_${{ job.target }}
-      dependsOn: rocprofiler_sdk_build_${{ job.target }}
+    - job: rocprofiler_sdk_test_${{ job.os }}_${{ job.target }}
+      dependsOn: rocprofiler_sdk_build_${{ job.os }}_${{ job.target }}
      condition:
        and(succeeded(),
          eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
@@ -177,6 +181,7 @@ jobs:
        parameters:
          aptPackages: ${{ parameters.aptPackages }}
          pipModules: ${{ parameters.pipModules }}
+          packageManager: ${{ job.packageManager }}
          registerROCmPackages: true
      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
@@ -188,6 +193,7 @@ jobs:
        parameters:
          checkoutRef: ${{ parameters.checkoutRef }}
          dependencyList: ${{ parameters.rocmDependencies }}
+          os: ${{ job.os }}
          gpuTarget: ${{ job.target }}
          ${{ if parameters.triggerDownstreamJobs }}:
              downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
@@ -202,6 +208,7 @@ jobs:
      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
        parameters:
          componentName: ${{ parameters.componentName }}
+          os: ${{ job.os }}
          extraBuildFlags: >-
            -DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm
            -DROCPROFILER_BUILD_TESTS=ON
@@ -213,7 +220,8 @@ jobs:
      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
        parameters:
          componentName: ${{ parameters.componentName }}
-          testDir: $(Agent.BuildDirectory)/s/build
+          os: ${{ job.os }}
+          testDir: $(Agent.BuildDirectory)/build
      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
        parameters:
          aptPackages: ${{ parameters.aptPackages }}
@@ -63,6 +63,7 @@ parameters:
    libopenblas-dev: openblas-devel
    libopenmpi-dev: openmpi-devel
    libpci-dev: libpciaccess-devel
+    libsimde-dev: simde-devel
    libssl-dev: openssl-devel
    # note: libstdc++-devel is in the base packages list
    libsystemd-dev: systemd-devel
@@ -35,8 +35,8 @@ parameters:
      developBranch: develop
      hasGpuTarget: true
    amdsmi:
-      pipelineId: 99
-      developBranch: amd-staging
+      pipelineId: 376
+      developBranch: develop
      hasGpuTarget: false
    aomp-extras:
      pipelineId: 111
@@ -115,7 +115,7 @@ parameters:
      developBranch: develop
      hasGpuTarget: true
    hipTensor:
-      pipelineId: 105
+      pipelineId: 374
      developBranch: develop
      hasGpuTarget: true
    llvm-project:
@@ -263,7 +263,7 @@ parameters:
      developBranch: develop
      hasGpuTarget: true
    rocWMMA:
-      pipelineId: 109
+      pipelineId: 370
      developBranch: develop
      hasGpuTarget: true
    rpp:
@@ -13,7 +13,7 @@ parameters:
  default: ctest
 - name: testParameters
  type: string
-  default: --output-on-failure --force-new-ctest-process --output-junit test_output.xml
+  default: --extra-verbose --output-on-failure --force-new-ctest-process --output-junit test_output.xml
 - name: extraTestParameters
  type: string
  default: ''
@@ -1,6 +1,7 @@
 .venv
 .vscode
 build
+__pycache__

 # documentation artifacts
 _build/
@@ -27,6 +27,7 @@ ASICs
 ASan
 ASAN
 ASm
+Async
 ATI
 atomicRMW
 AddressSanitizer
@@ -34,8 +35,11 @@ AlexNet
 Andrej
 Arb
 Autocast
+autograd
+Backported
 BARs
 BatchNorm
+BKC
 BLAS
 BMC
 BabelStream
@@ -50,6 +54,7 @@ CDNA
 CGUI
 CHTML
 CIFAR
+CNP
 CLI
 CLion
 CMake
@@ -77,6 +82,7 @@ CX
 Cavium
 CentOS
 ChatGPT
+Cholesky
 CoRR
 Codespaces
 Commitizen
@@ -86,10 +92,13 @@ Conda
 ConnectX
 CountOnes
 CuPy
+customizable
 da
 Dashboarding
 Dataloading
+dataflows
 DBRX
+DCQCN
 DDR
 DF
 DGEMM
@@ -104,8 +113,10 @@ DMA
 DOMContentLoaded
 DNN
 DNNL
+DOCA
 DPM
 DRI
+DSCP
 DW
 DWORD
 Dask
@@ -121,7 +132,9 @@ Deprecations
 DevCap
 DirectX
 Disaggregated
+disagg
 disaggregated
+disaggregation
 Dockerfile
 Dockerized
 Doxygen
@@ -130,10 +143,13 @@ ELMo
 ENDPGM
 EPYC
 ESXi
+EP
 EoS
 etcd
+equalto
 fas
 FBGEMM
+FiLM
 FIFOs
 FFT
 FFTs
@@ -149,15 +165,19 @@ Flang
 FlashAttention
 FlashInfer’s
 FlashInfer
+flashinfer
 FluxBenchmark
 Fortran
 Fuyu
 GALB
+GART
 GAT
+GATNE
 GCC
 GCD
 GCDs
 GCN
+GCNN
 GDB
 GDDR
 GDR
@@ -168,6 +188,8 @@ GFLOPS
 GFortran
 GFXIP
 GGUF
+GID
+Gbps
 Gemma
 GiB
 GIM
@@ -176,23 +198,30 @@ Glibc
 GLXT
 Gloo
 GMI
+GNN
+GNNs
 GPG
 GPR
 GPT
 GPU
 GPU's
+GPUDirect
+GPUVM
 GPUs
-Graphbolt
+GraphBolt
 GraphSage
 GRBM
 GRE
+GTT
 GenAI
 GenZ
 GitHub
 Gitpod
+hardcoded
 HBM
 HCA
 HGX
+HLO
 HIPCC
 hipDataType
 HIPExtension
@@ -212,7 +241,10 @@ Haswell
 Higgs
 href
 Hyperparameters
+HybridEngine
 Huggingface
+Hunyuan
+HunyuanVideo
 IB
 ICD
 ICT
@@ -229,6 +261,7 @@ IOP
 IOPS
 IOPM
 IOV
+IPs
 IRQ
 ISA
 ISV
@@ -243,7 +276,9 @@ Intersphinx
 Intra
 Ioffe
 JAX's
+JAXLIB
 Jinja
+js
 JSON
 Jupyter
 KFD
@@ -263,6 +298,8 @@ LLM
 LLMs
 LLVM
 LM
+logsumexp
+LPDDR
 LRU
 LSAN
 LSan
@@ -290,6 +327,7 @@ MNIST
 MPI
 MPT
 MSVC
+MTU
 mul
 MVAPICH
 MVFFR
@@ -298,6 +336,7 @@ Makefiles
 Matplotlib
 Matrox
 MaxText
+MBT
 Megablocks
 Megatrends
 Megatron
@@ -307,12 +346,16 @@ Meta's
 Miniconda
 MirroredStrategy
 Mixtral
+MLA
 MosaicML
 MoEs
 Mooncake
+MoRI
 Mpops
 Multicore
+multihost
 Multithreaded
+mx
 MXFP
 MyEnvironment
 MyST
@@ -349,6 +392,7 @@ OFED
 OMM
 OMP
 OMPI
+OOM
 OMPT
 OMPX
 ONNX
@@ -375,16 +419,22 @@ perf
 PEQT
 PIL
 PILImage
+PJRT
+PLDM
 POR
 PRNG
 PRs
+PSID
+PTPC
 PaLM
 Pageable
 PeerDirect
+Pensando
 PerfDb
 Perfetto
 PipelineParallel
 PnP
+Pollara
 PowerEdge
 PowerShell
 Pretrained
@@ -394,7 +444,9 @@ Profiler's
 PyPi
 Pytest
 PyTorch
+QPS
 Qcycles
+QoS
 Qwen
 RAII
 RAS
@@ -428,6 +480,7 @@ RPP
 RST
 RW
 Radeon
+Redfish
 RelWithDebInfo
 Req
 Rickle
@@ -474,6 +527,7 @@ Skylake
 Softmax
 Spack
 SplitK
+Strix
 Supermicro
 Szegedy
 TagRAM
@@ -484,6 +538,7 @@ TCI
 TCIU
 TCP
 TCR
+TTM
 TVM
 THREADGROUPS
 threadgroups
@@ -496,13 +551,12 @@ TPS
 TPU
 TPUs
 TSME
-Taichi
-Taichi's
 Tagram
 TensileLite
 TensorBoard
 TensorFlow
 TensorParallel
+TheRock
 ToC
 TorchAudio
 torchaudio
@@ -520,6 +574,7 @@ UAC
 UC
 UCC
 UCX
+ud
 UE
 UIF
 UMC
@@ -538,6 +593,9 @@ verl's
 VGPR
 VGPRs
 VM
+VMID
+VMIDs
+VMs
 VMEM
 VMWare
 VRAM
@@ -625,6 +683,7 @@ cmake
 cmd
 coalescable
 codename
+codenamed
 collater
 comgr
 compat
@@ -669,6 +728,7 @@ denoised
 denoises
 denormalize
 dequantization
+dequantized
 dequantizes
 deserializers
 detections
@@ -694,6 +754,7 @@ enqueue
 env
 epilog
 etcetera
+eth
 ethernet
 exascale
 executables
@@ -784,10 +845,12 @@ linalg
 linearized
 linter
 linux
+llm
 llvm
 lm
 localscratch
 logits
+loopback
 lossy
 macOS
 matchers
@@ -813,9 +876,11 @@ nanoGPT
 NCS
 NOP
 NVLink
+netplan
 num
 numref
 ocl
+openai
 opencl
 opencv
 openmp
@@ -829,11 +894,13 @@ pallas
 parallelization
 parallelizing
 param
+params
 parameterization
 passthrough
 pe
 perfcounter
 performant
+piecewise
 perl
 pragma
 pre
@@ -874,9 +941,11 @@ querySelectorAll
 queueing
 qwen
 radeon
+rc
 rccl
 rdc
 rdma
+reachability
 reStructuredText
 redirections
 refactorization
@@ -935,6 +1004,7 @@ scalability
 scalable
 scipy
 seealso
+selectattr
 selectedTag
 sendmsg
 seqs
@@ -945,6 +1015,7 @@ shader
 sharding
 sigmoid
 sles
+slurm
 sm
 smi
 softmax
@@ -980,6 +1051,7 @@ tokenizer
 tokenizes
 toolchain
 toolchains
+topk
 toolset
 toolsets
 torchtitan
@@ -995,6 +1067,7 @@ uncacheable
 uncorrectable
 underoptimized
 unhandled
+unfused
 uninstallation
 unmapped
 unsqueeze
@@ -1007,6 +1080,7 @@ USM
 UTCL
 UTIL
 utils
+UX
 vL
 variational
 vdi
@@ -1036,6 +1110,8 @@ writebacks
 wrreq
 wzo
 xargs
+xdit
+xDiT
 xGMI
 xPacked
 xz
@@ -1,33 +1,17 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <manifest>
    <remote name="rocm-org" fetch="https://github.com/ROCm/" />
-    <default revision="refs/tags/rocm-7.0.2"
+    <default revision="refs/tags/rocm-7.1.1"
     remote="rocm-org"
     sync-c="true"
     sync-j="4" />
 <!--list of projects for ROCm-->
    <project name="ROCK-Kernel-Driver" />
-    <project name="ROCR-Runtime" />
    <project name="amdsmi" />
-    <project name="aqlprofile" />
-    <project name="rdc" />
    <project name="rocm_bandwidth_test" />
-    <project name="rocm_smi_lib" />
-    <project name="rocm-core" />
    <project name="rocm-examples" />
-    <project name="rocminfo" />
-    <project name="rocprofiler" />
-    <project name="rocprofiler-register" />
-    <project name="rocprofiler-sdk" />
-    <project name="rocprofiler-compute" />
-    <project name="rocprofiler-systems" />
-    <project name="roctracer" />
 <!--HIP Projects-->
-    <project name="hip" />
-    <project name="hip-tests" />
    <project name="HIPIFY" />
-    <project name="clr" />
-    <project name="hipother" />
 <!-- The following projects are all associated with the AMDGPU LLVM compiler -->
    <project name="half" />
    <project name="llvm-project" />
@@ -41,6 +25,7 @@
    <project groups="mathlibs" name="MIVisionX" />
    <project groups="mathlibs" name="ROCmValidationSuite" />
    <project groups="mathlibs" name="composable_kernel" />
+    <project groups="mathlibs" name="hipSOLVER" />
    <project groups="mathlibs" name="hipTensor" />
    <project groups="mathlibs" name="hipfort" />
    <project groups="mathlibs" name="rccl" />
@@ -54,7 +39,14 @@
        MIOpen rocBLAS rocFFT rocPRIM rocRAND
        rocSPARSE rocThrust Tensile -->
    <project groups="mathlibs" name="rocm-libraries" />
+    <!-- The following components have been migrated to rocm-systems:
+        aqlprofile clr hip hip-tests hipother
+        rdc rocm-core rocm_smi_lib rocminfo rocprofiler-compute 
+        rocprofiler-register rocprofiler-sdk rocprofiler-systems 
+        rocprofiler rocr-runtime roctracer -->
+    <project groups="mathlibs" name="rocm-systems" />
    <project groups="mathlibs" name="rocPyDecode" />
+    <project groups="mathlibs" name="rocSOLVER" />
    <project groups="mathlibs" name="rocSHMEM" />
    <project groups="mathlibs" name="rocWMMA" />
    <project groups="mathlibs" name="rocm-cmake" />
@@ -25,69 +25,69 @@ additional licenses. Please review individual repositories for more information.
 <!-- spellcheck-disable -->
 | Component | License |
 |:---------------------|:-------------------------|
-| [AMD Compute Language Runtime (CLR)](https://github.com/ROCm/clr) | [MIT](https://github.com/ROCm/clr/blob/amd-staging/LICENSE.txt) |
+| [AMD Compute Language Runtime (CLR)](https://github.com/ROCm/rocm-systems/tree/develop/projects/clr) | [MIT](https://github.com/ROCm/rocm-systems/blob/develop/projects/clr/LICENSE.md) |
 | [AMD SMI](https://github.com/ROCm/amdsmi) | [MIT](https://github.com/ROCm/amdsmi/blob/amd-staging/LICENSE) |
 | [aomp](https://github.com/ROCm/aomp/) | [Apache 2.0](https://github.com/ROCm/aomp/blob/aomp-dev/LICENSE) |
 | [aomp-extras](https://github.com/ROCm/aomp-extras/) | [MIT](https://github.com/ROCm/aomp-extras/blob/aomp-dev/LICENSE) |
-| [AQLprofile](https://github.com/rocm/aqlprofile/) | [MIT](https://github.com/ROCm/aqlprofile/blob/amd-staging/LICENSE.md) |
+| [AQLprofile](https://github.com/ROCm/rocm-systems/tree/develop/projects/aqlprofile/) | [MIT](https://github.com/ROCm/rocm-systems/blob/develop/projects/aqlprofile/LICENSE.md) |
 | [Code Object Manager (Comgr)](https://github.com/ROCm/llvm-project/tree/amd-staging/amd/comgr) | [The University of Illinois/NCSA](https://github.com/ROCm/llvm-project/blob/amd-staging/amd/comgr/LICENSE.txt) |
 | [Composable Kernel](https://github.com/ROCm/composable_kernel) | [MIT](https://github.com/ROCm/composable_kernel/blob/develop/LICENSE) |
 | [half](https://github.com/ROCm/half/) | [MIT](https://github.com/ROCm/half/blob/rocm/LICENSE.txt) |
-| [HIP](https://github.com/ROCm/HIP/) | [MIT](https://github.com/ROCm/HIP/blob/amd-staging/LICENSE.txt) |
-| [hipamd](https://github.com/ROCm/clr/tree/amd-staging/hipamd) | [MIT](https://github.com/ROCm/clr/blob/amd-staging/hipamd/LICENSE.txt) |
-| [hipBLAS](https://github.com/ROCm/hipBLAS/) | [MIT](https://github.com/ROCm/hipBLAS/blob/develop/LICENSE.md) |
-| [hipBLASLt](https://github.com/ROCm/hipBLASLt/) | [MIT](https://github.com/ROCm/hipBLASLt/blob/develop/LICENSE.md) |
+| [HIP](https://github.com/ROCm/rocm-systems/tree/develop/projects/hip/) | [MIT](https://github.com/ROCm/rocm-systems/blob/develop/projects/hip/LICENSE.md) |
+| [hipamd](https://github.com/ROCm/rocm-systems/tree/develop/projects/clr/hipamd/) | [MIT](https://github.com/ROCm/rocm-systems/blob/develop/projects/clr/hipamd/LICENSE.md) |
+| [hipBLAS](https://github.com/ROCm/rocm-libraries/tree/develop/projects/hipblas/) | [MIT](https://github.com/ROCm/rocm-libraries/blob/develop/projects/hipblas/LICENSE.md) |
+| [hipBLASLt](https://github.com/ROCm/rocm-libraries/tree/develop/projects/hipblaslt/) | [MIT](https://github.com/ROCm/rocm-libraries/blob/develop/projects/hipblaslt/LICENSE.md) |
 | [HIPCC](https://github.com/ROCm/llvm-project/tree/amd-staging/amd/hipcc) | [MIT](https://github.com/ROCm/llvm-project/blob/amd-staging/amd/hipcc/LICENSE.txt) |
-| [hipCUB](https://github.com/ROCm/hipCUB/) | [Custom](https://github.com/ROCm/hipCUB/blob/develop/LICENSE.txt) |
-| [hipFFT](https://github.com/ROCm/hipFFT/) | [MIT](https://github.com/ROCm/hipFFT/blob/develop/LICENSE.md) |
+| [hipCUB](https://github.com/ROCm/rocm-libraries/tree/develop/projects/hipcub/) | [Custom](https://github.com/ROCm/rocm-libraries/blob/develop/projects/hipcub/LICENSE.txt) |
+| [hipFFT](https://github.com/ROCm/rocm-libraries/tree/develop/projects/hipfft/) | [MIT](https://github.com/ROCm/rocm-libraries/blob/develop/projects/hipfft/LICENSE.md) |
 | [hipfort](https://github.com/ROCm/hipfort/) | [MIT](https://github.com/ROCm/hipfort/blob/develop/LICENSE) |
 | [HIPIFY](https://github.com/ROCm/HIPIFY/) | [MIT](https://github.com/ROCm/HIPIFY/blob/amd-staging/LICENSE.txt) |
-| [hipRAND](https://github.com/ROCm/hipRAND/) | [MIT](https://github.com/ROCm/hipRAND/blob/develop/LICENSE.txt) |
-| [hipSOLVER](https://github.com/ROCm/hipSOLVER/) | [MIT](https://github.com/ROCm/hipSOLVER/blob/develop/LICENSE.md) |
-| [hipSPARSE](https://github.com/ROCm/hipSPARSE/) | [MIT](https://github.com/ROCm/hipSPARSE/blob/develop/LICENSE.md) |
-| [hipSPARSELt](https://github.com/ROCm/hipSPARSELt/) | [MIT](https://github.com/ROCm/hipSPARSELt/blob/develop/LICENSE.md) |
-| [hipTensor](https://github.com/ROCm/hipTensor) | [MIT](https://github.com/ROCm/hipTensor/blob/develop/LICENSE) |
+| [hipRAND](https://github.com/ROCm/rocm-libraries/tree/develop/projects/hiprand/) | [MIT](https://github.com/ROCm/rocm-libraries/blob/develop/projects/hiprand/LICENSE.md) |
+| [hipSOLVER](https://github.com/ROCm/rocm-libraries/tree/develop/projects/hipsolver/) | [MIT](https://github.com/ROCm/rocm-libraries/blob/develop/projects/hipsolver/LICENSE.md) |
+| [hipSPARSE](https://github.com/ROCm/rocm-libraries/tree/develop/projects/hipsparse/) | [MIT](https://github.com/ROCm/rocm-libraries/blob/develop/projects/hipsparse/LICENSE.md) |
+| [hipSPARSELt](https://github.com/ROCm/rocm-libraries/tree/develop/projects/hipsparselt/) | [MIT](https://github.com/ROCm/rocm-libraries/blob/develop/projects/hipsparselt/LICENSE.md) |
+| [hipTensor](https://github.com/ROCm/rocm-libraries/tree/develop/projects/hiptensor/) | [MIT](https://github.com/ROCm/rocm-libraries/blob/develop/projects/hiptensor/LICENSE) |
 | [llvm-project](https://github.com/ROCm/llvm-project/) | [Apache](https://github.com/ROCm/llvm-project/blob/amd-staging/LICENSE.TXT) |
 | [llvm-project/flang](https://github.com/ROCm/llvm-project/tree/amd-staging/flang) | [Apache 2.0](https://github.com/ROCm/llvm-project/blob/amd-staging/flang/LICENSE.TXT) |
 | [MIGraphX](https://github.com/ROCm/AMDMIGraphX/) | [MIT](https://github.com/ROCm/AMDMIGraphX/blob/develop/LICENSE) |
-| [MIOpen](https://github.com/ROCm/MIOpen/) | [MIT](https://github.com/ROCm/rocm-libraries/blob/develop/projects/miopen/LICENSE.md) |
+| [MIOpen](https://github.com/ROCm/rocm-libraries/tree/develop/projects/miopen/) | [MIT](https://github.com/ROCm/rocm-libraries/blob/develop/projects/miopen/LICENSE.md) |
 | [MIVisionX](https://github.com/ROCm/MIVisionX/) | [MIT](https://github.com/ROCm/MIVisionX/blob/develop/LICENSE.txt) |
 | [rocAL](https://github.com/ROCm/rocAL) | [MIT](https://github.com/ROCm/rocAL/blob/develop/LICENSE.txt) |
 | [rocALUTION](https://github.com/ROCm/rocALUTION/) | [MIT](https://github.com/ROCm/rocALUTION/blob/develop/LICENSE.md) |
-| [rocBLAS](https://github.com/ROCm/rocBLAS/) | [MIT](https://github.com/ROCm/rocBLAS/blob/develop/LICENSE.md) |
+| [rocBLAS](https://github.com/ROCm/rocm-libraries/tree/develop/projects/rocblas/) | [MIT](https://github.com/ROCm/rocm-libraries/blob/develop/projects/rocblas/LICENSE.md) |
 | [ROCdbgapi](https://github.com/ROCm/ROCdbgapi/) | [MIT](https://github.com/ROCm/ROCdbgapi/blob/amd-staging/LICENSE.txt) |
 | [rocDecode](https://github.com/ROCm/rocDecode) | [MIT](https://github.com/ROCm/rocDecode/blob/develop/LICENSE) |
-| [rocFFT](https://github.com/ROCm/rocFFT/) | [MIT](https://github.com/ROCm/rocFFT/blob/develop/LICENSE.md) |
+| [rocFFT](https://github.com/ROCm/rocm-libraries/tree/develop/projects/rocfft/) | [MIT](https://github.com/ROCm/rocm-libraries/blob/develop/projects/rocfft/LICENSE.md) |
 | [ROCgdb](https://github.com/ROCm/ROCgdb/) | [GNU General Public License v3.0](https://github.com/ROCm/ROCgdb/blob/amd-staging/COPYING3) |
 | [rocJPEG](https://github.com/ROCm/rocJPEG/) | [MIT](https://github.com/ROCm/rocJPEG/blob/develop/LICENSE) |
 | [ROCK-Kernel-Driver](https://github.com/ROCm/ROCK-Kernel-Driver/) | [GPL 2.0 WITH Linux-syscall-note](https://github.com/ROCm/ROCK-Kernel-Driver/blob/master/COPYING) |
-| [rocminfo](https://github.com/ROCm/rocminfo/) | [The University of Illinois/NCSA](https://github.com/ROCm/rocminfo/blob/amd-staging/License.txt) |
+| [rocminfo](https://github.com/ROCm/rocm-systems/tree/develop/projects/rocminfo/) | [The University of Illinois/NCSA](https://github.com/ROCm/rocm-systems/blob/develop/projects/rocminfo/License.txt) |
 | [ROCm Bandwidth Test](https://github.com/ROCm/rocm_bandwidth_test/) | [MIT](https://github.com/ROCm/rocm_bandwidth_test/blob/master/LICENSE.txt) |
 | [ROCm CMake](https://github.com/ROCm/rocm-cmake/) | [MIT](https://github.com/ROCm/rocm-cmake/blob/develop/LICENSE) |
 | [ROCm Communication Collectives Library (RCCL)](https://github.com/ROCm/rccl/) | [Custom](https://github.com/ROCm/rccl/blob/develop/LICENSE.txt) |
-| [ROCm-Core](https://github.com/ROCm/rocm-core) | [MIT](https://github.com/ROCm/rocm-core/blob/master/copyright) |
-| [ROCm Compute Profiler](https://github.com/ROCm/rocprofiler-compute) | [MIT](https://github.com/ROCm/rocprofiler-compute/blob/amd-staging/LICENSE) |
-| [ROCm Data Center (RDC)](https://github.com/ROCm/rdc/) | [MIT](https://github.com/ROCm/rdc/blob/amd-staging/LICENSE.md) |
+| [ROCm-Core](https://github.com/ROCm/rocm-systems/tree/develop/projects/rocm-core/) | [MIT](https://github.com/ROCm/rocm-systems/blob/develop/projects/rocm-core/LICENSE.md) |
+| [ROCm Compute Profiler](https://github.com/ROCm/rocm-systems/tree/develop/projects/rocprofiler-compute/) | [MIT](https://github.com/ROCm/rocm-systems/blob/develop/projects/rocprofiler-compute/LICENSE.md) |
+| [ROCm Data Center (RDC)](https://github.com/ROCm/rocm-systems/tree/develop/projects/rdc/) | [MIT](https://github.com/ROCm/rocm-systems/blob/develop/projects/rdc/LICENSE.md) |
 | [ROCm-Device-Libs](https://github.com/ROCm/llvm-project/tree/amd-staging/amd/device-libs) | [The University of Illinois/NCSA](https://github.com/ROCm/llvm-project/blob/amd-staging/amd/device-libs/LICENSE.TXT) |
-| [ROCm-OpenCL-Runtime](https://github.com/ROCm/clr/tree/amd-staging/opencl) | [MIT](https://github.com/ROCm/clr/blob/amd-staging/opencl/LICENSE.txt) |
+| [ROCm-OpenCL-Runtime](https://github.com/ROCm/rocm-systems/tree/develop/projects/clr/opencl/) | [MIT](https://github.com/ROCm/rocm-systems/blob/develop/projects/clr/opencl/LICENSE.md) |
 | [ROCm Performance Primitives (RPP)](https://github.com/ROCm/rpp) | [MIT](https://github.com/ROCm/rpp/blob/develop/LICENSE) |
-| [ROCm SMI Lib](https://github.com/ROCm/rocm_smi_lib/) | [MIT](https://github.com/ROCm/rocm_smi_lib/blob/amd-staging/LICENSE.md) |
-| [ROCm Systems Profiler](https://github.com/ROCm/rocprofiler-systems) | [MIT](https://github.com/ROCm/rocprofiler-systems/blob/amd-staging/LICENSE.md) |
+| [ROCm SMI Lib](https://github.com/ROCm/rocm-systems/tree/develop/projects/rocm-smi-lib/) | [MIT](https://github.com/ROCm/rocm-systems/blob/develop/projects/rocm-smi-lib/LICENSE.md) |
+| [ROCm Systems Profiler](https://github.com/ROCm/rocm-systems/tree/develop/projects/rocprofiler-systems/) | [MIT](https://github.com/ROCm/rocm-systems/blob/develop/projects/rocprofiler-systems/LICENSE.md) |
 | [ROCm Validation Suite](https://github.com/ROCm/ROCmValidationSuite/) | [MIT](https://github.com/ROCm/ROCmValidationSuite/blob/master/LICENSE) |
-| [rocPRIM](https://github.com/ROCm/rocPRIM/) | [MIT](https://github.com/ROCm/rocPRIM/blob/develop/LICENSE.txt) |
-| [ROCProfiler](https://github.com/ROCm/rocprofiler/) | [MIT](https://github.com/ROCm/rocprofiler/blob/amd-staging/LICENSE.md) |
-| [ROCprofiler-SDK](https://github.com/ROCm/rocprofiler-sdk) | [MIT](https://github.com/ROCm/rocprofiler-sdk/blob/amd-mainline/LICENSE) |
+| [rocPRIM](https://github.com/ROCm/rocm-libraries/tree/develop/projects/rocprim/) | [MIT](https://github.com/ROCm/rocm-libraries/blob/develop/projects/rocprim/LICENSE.md) |
+| [ROCProfiler](https://github.com/ROCm/rocm-systems/tree/develop/projects/rocprofiler/) | [MIT](https://github.com/ROCm/rocm-systems/blob/develop/projects/rocprofiler/LICENSE.md) |
+| [ROCprofiler-SDK](https://github.com/ROCm/rocm-systems/tree/develop/projects/rocprofiler-sdk/) | [MIT](https://github.com/ROCm/rocm-systems/blob/develop/projects/rocprofiler-sdk/LICENSE.md) |
 | [rocPyDecode](https://github.com/ROCm/rocPyDecode) | [MIT](https://github.com/ROCm/rocPyDecode/blob/develop/LICENSE.txt) |
-| [rocRAND](https://github.com/ROCm/rocRAND/) | [MIT](https://github.com/ROCm/rocRAND/blob/develop/LICENSE.txt) |
+| [rocRAND](https://github.com/ROCm/rocm-libraries/tree/develop/projects/rocrand/) | [MIT](https://github.com/ROCm/rocm-libraries/blob/develop/projects/rocrand/LICENSE.md) |
 | [ROCr Debug Agent](https://github.com/ROCm/rocr_debug_agent/) | [The University of Illinois/NCSA](https://github.com/ROCm/rocr_debug_agent/blob/amd-staging/LICENSE.txt) |
-| [ROCR-Runtime](https://github.com/ROCm/ROCR-Runtime/) | [The University of Illinois/NCSA](https://github.com/ROCm/ROCR-Runtime/blob/amd-staging/LICENSE.txt) |
+| [ROCR-Runtime](https://github.com/ROCm/rocm-systems/tree/develop/projects/rocr-runtime/) | [The University of Illinois/NCSA](https://github.com/ROCm/rocm-systems/blob/develop/projects/rocr-runtime/LICENSE.txt) |
 | [rocSHMEM](https://github.com/ROCm/rocSHMEM/) | [MIT](https://github.com/ROCm/rocSHMEM/blob/develop/LICENSE.md) |
-| [rocSOLVER](https://github.com/ROCm/rocSOLVER/) | [BSD-2-Clause](https://github.com/ROCm/rocSOLVER/blob/develop/LICENSE.md) |
-| [rocSPARSE](https://github.com/ROCm/rocSPARSE/) | [MIT](https://github.com/ROCm/rocSPARSE/blob/develop/LICENSE.md) |
-| [rocThrust](https://github.com/ROCm/rocThrust/) | [Apache 2.0](https://github.com/ROCm/rocThrust/blob/develop/LICENSE) |
-| [ROCTracer](https://github.com/ROCm/roctracer/) | [MIT](https://github.com/ROCm/roctracer/blob/amd-master/LICENSE) |
-| [rocWMMA](https://github.com/ROCm/rocWMMA/) | [MIT](https://github.com/ROCm/rocWMMA/blob/develop/LICENSE.md) |
-| [Tensile](https://github.com/ROCm/Tensile/) | [MIT](https://github.com/ROCm/Tensile/blob/develop/LICENSE.md) |
+| [rocSOLVER](https://github.com/ROCm/rocm-libraries/tree/develop/projects/rocsolver/) | [BSD-2-Clause](https://github.com/ROCm/rocm-libraries/blob/develop/projects/rocsolver/LICENSE.md) |
+| [rocSPARSE](https://github.com/ROCm/rocm-libraries/tree/develop/projects/rocsparse/) | [MIT](https://github.com/ROCm/rocm-libraries/blob/develop/projects/rocsparse/LICENSE.md) |
+| [rocThrust](https://github.com/ROCm/rocm-libraries/tree/develop/projects/rocthrust/) | [Apache 2.0](https://github.com/ROCm/rocm-libraries/blob/develop/projects/rocthrust/LICENSE) |
+| [ROCTracer](https://github.com/ROCm/rocm-systems/tree/develop/projects/roctracer/) | [MIT](https://github.com/ROCm/rocm-systems/blob/develop/projects/roctracer/LICENSE.md) |
+| [rocWMMA](https://github.com/ROCm/rocm-libraries/tree/develop/projects/rocwmma/) | [MIT](https://github.com/ROCm/rocm-libraries/blob/develop/projects/rocwmma/LICENSE.md) |
+| [Tensile](https://github.com/ROCm/rocm-libraries/tree/develop/shared/tensile/) | [MIT](https://github.com/ROCm/rocm-libraries/blob/develop/shared/tensile/LICENSE.md) |
 | [TransferBench](https://github.com/ROCm/TransferBench) | [MIT](https://github.com/ROCm/TransferBench/blob/develop/LICENSE.md) |

 Open sourced ROCm components are released via public GitHub
@@ -1,137 +1,130 @@
-ROCm Version,7.0.2,7.0.1/7.0.0,6.4.3,6.4.2,6.4.1,6.4.0,6.3.3,6.3.2,6.3.1,6.3.0,6.2.4,6.2.2,6.2.1,6.2.0, 6.1.5, 6.1.2, 6.1.1, 6.1.0, 6.0.2, 6.0.0
-      :ref:`Operating systems & kernels <OS-kernel-versions>`,Ubuntu 24.04.3,Ubuntu 24.04.3,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,"Ubuntu 24.04.1, 24.04","Ubuntu 24.04.1, 24.04","Ubuntu 24.04.1, 24.04",Ubuntu 24.04,,,,,,
-      ,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,"Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3, 22.04.2","Ubuntu 22.04.4, 22.04.3, 22.04.2"
-      ,,,,,,,,,,,,,,,"Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5"
-      ,"RHEL 10.0 [#rhel-10-702-past-60]_, 9.6 [#rhel-10-702-past-60]_, 9.4 [#rhel-94-702-past-60]_","RHEL 9.6 [#rhel-10-702-past-60]_, 9.4 [#rhel-94-702-past-60]_","RHEL 9.6, 9.4","RHEL 9.6, 9.4","RHEL 9.6, 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.4, 9.3","RHEL 9.4, 9.3","RHEL 9.4, 9.3","RHEL 9.4, 9.3","RHEL 9.4, 9.3, 9.2","RHEL 9.4, 9.3, 9.2","RHEL 9.4, 9.3, 9.2","RHEL 9.4, 9.3, 9.2","RHEL 9.3, 9.2","RHEL 9.3, 9.2"
-      ,RHEL 8.10 [#rhel-700-past-60]_,RHEL 8.10 [#rhel-700-past-60]_,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,"RHEL 8.10, 8.9","RHEL 8.10, 8.9","RHEL 8.10, 8.9","RHEL 8.10, 8.9","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8"
-      ,SLES 15 SP7 [#sles-db-700-past-60]_,SLES 15 SP7 [#sles-db-700-past-60]_,"SLES 15 SP7, SP6","SLES 15 SP7, SP6",SLES 15 SP6,SLES 15 SP6,"SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4"
-      ,,,,,,,,,,,,,,,,CentOS 7.9,CentOS 7.9,CentOS 7.9,CentOS 7.9,CentOS 7.9
-      ,"Oracle Linux 10, 9, 8 [#ol-700-mi300x-past-60]_","Oracle Linux 9, 8 [#ol-700-mi300x-past-60]_","Oracle Linux 9, 8 [#mi300x-past-60]_","Oracle Linux 9, 8 [#mi300x-past-60]_","Oracle Linux 9, 8 [#mi300x-past-60]_","Oracle Linux 9, 8 [#mi300x-past-60]_",Oracle Linux 8.10 [#mi300x-past-60]_,Oracle Linux 8.10 [#mi300x-past-60]_,Oracle Linux 8.10 [#mi300x-past-60]_,Oracle Linux 8.10 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,,,
-      ,"Debian 13 [#db-mi300x-past-60]_, 12 [#sles-db-700-past-60]_",Debian 12 [#sles-db-700-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,,,,,,,,,,,
-      ,Azure Linux 3.0 [#az-mi300x-past-60]_,Azure Linux 3.0 [#az-mi300x-past-60]_,Azure Linux 3.0 [#az-mi300x-past-60]_,Azure Linux 3.0 [#az-mi300x-past-60]_,Azure Linux 3.0 [#az-mi300x-past-60]_,Azure Linux 3.0 [#az-mi300x-past-60]_,Azure Linux 3.0 [#az-mi300x-630-past-60]_,Azure Linux 3.0 [#az-mi300x-630-past-60]_,,,,,,,,,,,,
-      ,Rocky Linux 9 [#rl-700-past-60]_,Rocky Linux 9 [#rl-700-past-60]_,,,,,,,,,,,,,,,,,,
-      ,.. _architecture-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,
-      :doc:`Architecture <rocm-install-on-linux:reference/system-requirements>`,CDNA4,CDNA4,,,,,,,,,,,,,,,,,,
-      ,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3
-      ,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2
-      ,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA
-      ,RDNA4,RDNA4,RDNA4,RDNA4,RDNA4,,,,,,,,,,,,,,,
-      ,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3
-      ,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2
-      ,.. _gpu-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,
-      :doc:`GPU / LLVM target <rocm-install-on-linux:reference/system-requirements>`,gfx950 [#mi350x-os-past-60]_,gfx950 [#mi350x-os-past-60]_,,,,,,,,,,,,,,,,,,
-      ,gfx1201 [#RDNA-OS-700-past-60]_,gfx1201 [#RDNA-OS-700-past-60]_,gfx1201 [#RDNA-OS-past-60]_,gfx1201 [#RDNA-OS-past-60]_,gfx1201 [#RDNA-OS-past-60]_,,,,,,,,,,,,,,,
-      ,gfx1200 [#RDNA-OS-700-past-60]_,gfx1200 [#RDNA-OS-700-past-60]_,gfx1200 [#RDNA-OS-past-60]_,gfx1200 [#RDNA-OS-past-60]_,gfx1200 [#RDNA-OS-past-60]_,,,,,,,,,,,,,,,
-      ,gfx1101 [#RDNA-OS-700-past-60]_ [#rd-v710-past-60]_,gfx1101 [#RDNA-OS-700-past-60]_ [#rd-v710-past-60]_,gfx1101 [#RDNA-OS-past-60]_ [#7700XT-OS-past-60]_,gfx1101 [#RDNA-OS-past-60]_ [#7700XT-OS-past-60]_,gfx1101 [#RDNA-OS-past-60]_,,,,,,,,,,,,,,,
-      ,gfx1100 [#RDNA-OS-700-past-60]_,gfx1100 [#RDNA-OS-700-past-60]_,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100
-      ,gfx1030 [#RDNA-OS-700-past-60]_ [#rd-v620-past-60]_,gfx1030 [#RDNA-OS-700-past-60]_ [#rd-v620-past-60]_,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030
-      ,gfx942 [#mi325x-os-past-60]_ [#mi300x-os-past-60]_ [#mi300A-os-past-60]_,gfx942 [#mi325x-os-past-60]_ [#mi300x-os-past-60]_ [#mi300A-os-past-60]_,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942 [#mi300_624-past-60]_,gfx942 [#mi300_622-past-60]_,gfx942 [#mi300_621-past-60]_,gfx942 [#mi300_620-past-60]_, gfx942 [#mi300_612-past-60]_, gfx942 [#mi300_612-past-60]_, gfx942 [#mi300_611-past-60]_, gfx942 [#mi300_610-past-60]_, gfx942 [#mi300_602-past-60]_, gfx942 [#mi300_600-past-60]_
-      ,gfx90a [#mi200x-os-past-60]_,gfx90a [#mi200x-os-past-60]_,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a
-      ,gfx908 [#mi100-os-past-60]_,gfx908 [#mi100-os-past-60]_,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908
-      ,,,,,,,,,,,,,,,,,,,,
-      FRAMEWORK SUPPORT,.. _framework-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,
-      :doc:`PyTorch <../compatibility/ml-compatibility/pytorch-compatibility>`,"2.8, 2.7, 2.6","2.7, 2.6, 2.5","2.6, 2.5, 2.4, 2.3","2.6, 2.5, 2.4, 2.3","2.6, 2.5, 2.4, 2.3","2.6, 2.5, 2.4, 2.3","2.4, 2.3, 2.2, 1.13","2.4, 2.3, 2.2, 1.13","2.4, 2.3, 2.2, 1.13","2.4, 2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13"
-      :doc:`TensorFlow <../compatibility/ml-compatibility/tensorflow-compatibility>`,"2.19.1, 2.18.1, 2.17.1 [#tf-mi350-past-60]_","2.19.1, 2.18.1, 2.17.1 [#tf-mi350-past-60]_","2.18.1, 2.17.1, 2.16.2","2.18.1, 2.17.1, 2.16.2","2.18.1, 2.17.1, 2.16.2","2.18.1, 2.17.1, 2.16.2","2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.14.0, 2.13.1, 2.12.1","2.14.0, 2.13.1, 2.12.1"
-      :doc:`JAX <../compatibility/ml-compatibility/jax-compatibility>`,0.6.0,0.6.0,0.4.35,0.4.35,0.4.35,0.4.35,0.4.31,0.4.31,0.4.31,0.4.31,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26
-      :doc:`verl <../compatibility/ml-compatibility/verl-compatibility>` [#verl_compat-past-60]_,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,0.3.0.post0,N/A,N/A,N/A,N/A,N/A,N/A
-      :doc:`Stanford Megatron-LM <../compatibility/ml-compatibility/stanford-megatron-lm-compatibility>` [#stanford-megatron-lm_compat-past-60]_,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,85f95ae,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
-      :doc:`DGL <../compatibility/ml-compatibility/dgl-compatibility>` [#dgl_compat-past-60]_,N/A,N/A,N/A,N/A,N/A,2.4.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
-      :doc:`Megablocks <../compatibility/ml-compatibility/megablocks-compatibility>` [#megablocks_compat-past-60]_,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,0.7.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
-      :doc:`Taichi <../compatibility/ml-compatibility/taichi-compatibility>` [#taichi_compat-past-60]_,N/A,N/A,N/A,N/A,N/A,N/A,N/A,1.8.0b1,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
-      :doc:`Ray <../compatibility/ml-compatibility/ray-compatibility>` [#ray_compat-past-60]_,N/A,N/A,N/A,N/A,2.48.0.post0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
-      :doc:`llama.cpp <../compatibility/ml-compatibility/llama-cpp-compatibility>` [#llama-cpp_compat-past-60]_,N/A,b6356,b6356,b6356,b6356,b5997,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
-      :doc:`FlashInfer <../compatibility/ml-compatibility/flashinfer-compatibility>` [#flashinfer_compat-past-60]_,N/A,N/A,N/A,N/A,v0.2.5,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
-      `ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,1.22.0,1.22.0,1.20.0,1.20.0,1.20.0,1.20.0,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.14.1,1.14.1
-      ,,,,,,,,,,,,,,,,,,,,
-      ,,,,,,,,,,,,,,,,,,,,
-      THIRD PARTY COMMS,.. _thirdpartycomms-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,
-      `UCC <https://github.com/ROCm/ucc>`_,>=1.4.0,>=1.4.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.2.0,>=1.2.0
-      `UCX <https://github.com/ROCm/ucx>`_,>=1.17.0,>=1.17.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.14.1,>=1.14.1,>=1.14.1,>=1.14.1,>=1.14.1,>=1.14.1
-      ,,,,,,,,,,,,,,,,,,,,
-      THIRD PARTY ALGORITHM,.. _thirdpartyalgorithm-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,
-      Thrust,2.6.0,2.6.0,2.5.0,2.5.0,2.5.0,2.5.0,2.3.2,2.3.2,2.3.2,2.3.2,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.1,2.0.1
-      CUB,2.6.0,2.6.0,2.5.0,2.5.0,2.5.0,2.5.0,2.3.2,2.3.2,2.3.2,2.3.2,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.1,2.0.1
-      ,,,,,,,,,,,,,,,,,,,,
-     DRIVER & USER SPACE [#kfd_support-past-60]_,.. _kfd-userspace-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,
-      :doc:`AMD GPU Driver <rocm-install-on-linux:reference/user-kernel-space-compat-matrix>`,"30.10.2, 30.10.1 [#driver_patch-past-60]_, 30.10, 6.4.x, 6.3.x","30.10.1 [#driver_patch-past-60]_, 30.10, 6.4.x, 6.3.x, 6.2.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.2.x, 6.1.x, 6.0.x, 5.7.x, 5.6.x","6.2.x, 6.1.x, 6.0.x, 5.7.x, 5.6.x"
-      ,,,,,,,,,,,,,,,,,,,,
-      ML & COMPUTER VISION,.. _mllibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,
-      :doc:`Composable Kernel <composable_kernel:index>`,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0
-      :doc:`MIGraphX <amdmigraphx:index>`,2.13.0,2.13.0,2.12.0,2.12.0,2.12.0,2.12.0,2.11.0,2.11.0,2.11.0,2.11.0,2.10.0,2.10.0,2.10.0,2.10.0,2.9.0,2.9.0,2.9.0,2.9.0,2.8.0,2.8.0
-      :doc:`MIOpen <miopen:index>`,3.5.0,3.5.0,3.4.0,3.4.0,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.0,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
-      :doc:`MIVisionX <mivisionx:index>`,3.3.0,3.3.0,3.2.0,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0,3.0.0,3.0.0,2.5.0,2.5.0,2.5.0,2.5.0,2.5.0,2.5.0
-      :doc:`rocAL <rocal:index>`,2.3.0,2.3.0,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.0,2.0.0,2.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
-      :doc:`rocDecode <rocdecode:index>`,1.0.0,1.0.0,0.10.0,0.10.0,0.10.0,0.10.0,0.8.0,0.8.0,0.8.0,0.8.0,0.6.0,0.6.0,0.6.0,0.6.0,0.6.0,0.6.0,0.5.0,0.5.0,N/A,N/A
-      :doc:`rocJPEG <rocjpeg:index>`,1.1.0,1.1.0,0.8.0,0.8.0,0.8.0,0.8.0,0.6.0,0.6.0,0.6.0,0.6.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
-      :doc:`rocPyDecode <rocpydecode:index>`,0.6.0,0.6.0,0.3.1,0.3.1,0.3.1,0.3.1,0.2.0,0.2.0,0.2.0,0.2.0,0.1.0,0.1.0,0.1.0,0.1.0,N/A,N/A,N/A,N/A,N/A,N/A
-      :doc:`RPP <rpp:index>`,2.0.0,2.0.0,1.9.10,1.9.10,1.9.10,1.9.10,1.9.1,1.9.1,1.9.1,1.9.1,1.8.0,1.8.0,1.8.0,1.8.0,1.5.0,1.5.0,1.5.0,1.5.0,1.4.0,1.4.0
-      ,,,,,,,,,,,,,,,,,,,,
-      COMMUNICATION,.. _commlibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,
-      :doc:`RCCL <rccl:index>`,2.26.6,2.26.6,2.22.3,2.22.3,2.22.3,2.22.3,2.21.5,2.21.5,2.21.5,2.21.5,2.20.5,2.20.5,2.20.5,2.20.5,2.18.6,2.18.6,2.18.6,2.18.6,2.18.3,2.18.3
-      :doc:`rocSHMEM <rocshmem:index>`,3.0.0,3.0.0,2.0.1,2.0.1,2.0.0,2.0.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
-      ,,,,,,,,,,,,,,,,,,,,
-      MATH LIBS,.. _mathlibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,
-      `half <https://github.com/ROCm/half>`_ ,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0
-      :doc:`hipBLAS <hipblas:index>`,3.0.2,3.0.0,2.4.0,2.4.0,2.4.0,2.4.0,2.3.0,2.3.0,2.3.0,2.3.0,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.0,2.0.0
-      :doc:`hipBLASLt <hipblaslt:index>`,1.0.0,1.0.0,0.12.1,0.12.1,0.12.1,0.12.0,0.10.0,0.10.0,0.10.0,0.10.0,0.8.0,0.8.0,0.8.0,0.8.0,0.7.0,0.7.0,0.7.0,0.7.0,0.6.0,0.6.0
-      :doc:`hipFFT <hipfft:index>`,1.0.20,1.0.20,1.0.18,1.0.18,1.0.18,1.0.18,1.0.17,1.0.17,1.0.17,1.0.17,1.0.16,1.0.15,1.0.15,1.0.14,1.0.14,1.0.14,1.0.14,1.0.14,1.0.13,1.0.13
-      :doc:`hipfort <hipfort:index>`,0.7.0,0.7.0,0.6.0,0.6.0,0.6.0,0.6.0,0.5.1,0.5.1,0.5.0,0.5.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0
-      :doc:`hipRAND <hiprand:index>`,3.0.0,3.0.0,2.12.0,2.12.0,2.12.0,2.12.0,2.11.1,2.11.1,2.11.1,2.11.0,2.11.1,2.11.0,2.11.0,2.11.0,2.10.16,2.10.16,2.10.16,2.10.16,2.10.16,2.10.16
-      :doc:`hipSOLVER <hipsolver:index>`,3.0.0,3.0.0,2.4.0,2.4.0,2.4.0,2.4.0,2.3.0,2.3.0,2.3.0,2.3.0,2.2.0,2.2.0,2.2.0,2.2.0,2.1.1,2.1.1,2.1.1,2.1.0,2.0.0,2.0.0
-      :doc:`hipSPARSE <hipsparse:index>`,4.0.1,4.0.1,3.2.0,3.2.0,3.2.0,3.2.0,3.1.2,3.1.2,3.1.2,3.1.2,3.1.1,3.1.1,3.1.1,3.1.1,3.0.1,3.0.1,3.0.1,3.0.1,3.0.0,3.0.0
-      :doc:`hipSPARSELt <hipsparselt:index>`,0.2.4,0.2.4,0.2.3,0.2.3,0.2.3,0.2.3,0.2.2,0.2.2,0.2.2,0.2.2,0.2.1,0.2.1,0.2.1,0.2.1,0.2.0,0.2.0,0.1.0,0.1.0,0.1.0,0.1.0
-      :doc:`rocALUTION <rocalution:index>`,4.0.0,4.0.0,3.2.3,3.2.3,3.2.3,3.2.2,3.2.1,3.2.1,3.2.1,3.2.1,3.2.1,3.2.0,3.2.0,3.2.0,3.1.1,3.1.1,3.1.1,3.1.1,3.0.3,3.0.3
-      :doc:`rocBLAS <rocblas:index>`,5.0.2,5.0.0,4.4.1,4.4.1,4.4.0,4.4.0,4.3.0,4.3.0,4.3.0,4.3.0,4.2.4,4.2.1,4.2.1,4.2.0,4.1.2,4.1.2,4.1.0,4.1.0,4.0.0,4.0.0
-      :doc:`rocFFT <rocfft:index>`,1.0.34,1.0.34,1.0.32,1.0.32,1.0.32,1.0.32,1.0.31,1.0.31,1.0.31,1.0.31,1.0.30,1.0.29,1.0.29,1.0.28,1.0.27,1.0.27,1.0.27,1.0.26,1.0.25,1.0.23
-      :doc:`rocRAND <rocrand:index>`,4.0.0,4.0.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.0,3.2.0,3.2.0,3.2.0,3.1.1,3.1.0,3.1.0,3.1.0,3.0.1,3.0.1,3.0.1,3.0.1,3.0.0,2.10.17
-      :doc:`rocSOLVER <rocsolver:index>`,3.30.1,3.30.0,3.28.2,3.28.2,3.28.0,3.28.0,3.27.0,3.27.0,3.27.0,3.27.0,3.26.2,3.26.0,3.26.0,3.26.0,3.25.0,3.25.0,3.25.0,3.25.0,3.24.0,3.24.0
-      :doc:`rocSPARSE <rocsparse:index>`,4.0.2,4.0.2,3.4.0,3.4.0,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.1,3.2.0,3.2.0,3.2.0,3.1.2,3.1.2,3.1.2,3.1.2,3.0.2,3.0.2
-      :doc:`rocWMMA <rocwmma:index>`,2.0.0,2.0.0,1.7.0,1.7.0,1.7.0,1.7.0,1.6.0,1.6.0,1.6.0,1.6.0,1.5.0,1.5.0,1.5.0,1.5.0,1.4.0,1.4.0,1.4.0,1.4.0,1.3.0,1.3.0
-      :doc:`Tensile <tensile:src/index>`,4.44.0,4.44.0,4.43.0,4.43.0,4.43.0,4.43.0,4.42.0,4.42.0,4.42.0,4.42.0,4.41.0,4.41.0,4.41.0,4.41.0,4.40.0,4.40.0,4.40.0,4.40.0,4.39.0,4.39.0
-      ,,,,,,,,,,,,,,,,,,,,
-      PRIMITIVES,.. _primitivelibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,
-      :doc:`hipCUB <hipcub:index>`,4.0.0,4.0.0,3.4.0,3.4.0,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.1,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
-      :doc:`hipTensor <hiptensor:index>`,2.0.0,2.0.0,1.5.0,1.5.0,1.5.0,1.5.0,1.4.0,1.4.0,1.4.0,1.4.0,1.3.0,1.3.0,1.3.0,1.3.0,1.2.0,1.2.0,1.2.0,1.2.0,1.1.0,1.1.0
-      :doc:`rocPRIM <rocprim:index>`,4.0.1,4.0.0,3.4.1,3.4.1,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.2,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
-      :doc:`rocThrust <rocthrust:index>`,4.0.0,4.0.0,3.3.0,3.3.0,3.3.0,3.3.0,3.3.0,3.3.0,3.3.0,3.3.0,3.1.1,3.1.0,3.1.0,3.0.1,3.0.1,3.0.1,3.0.1,3.0.1,3.0.0,3.0.0
-      ,,,,,,,,,,,,,,,,,,,,
-      SUPPORT LIBS,,,,,,,,,,,,,,,,,,,,
-      `hipother <https://github.com/ROCm/hipother>`_,7.0.51830,7.0.51830,6.4.43483,6.4.43483,6.4.43483,6.4.43482,6.3.42134,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
-      `rocm-core <https://github.com/ROCm/rocm-core>`_,7.0.2,7.0.1/7.0.0,6.4.3,6.4.2,6.4.1,6.4.0,6.3.3,6.3.2,6.3.1,6.3.0,6.2.4,6.2.2,6.2.1,6.2.0,6.1.5,6.1.2,6.1.1,6.1.0,6.0.2,6.0.0
-      `ROCT-Thunk-Interface <https://github.com/ROCm/ROCT-Thunk-Interface>`_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,20240607.5.7,20240607.5.7,20240607.4.05,20240607.1.4246,20240125.5.08,20240125.5.08,20240125.5.08,20240125.3.30,20231016.2.245,20231016.2.245
-      ,,,,,,,,,,,,,,,,,,,,
-      SYSTEM MGMT TOOLS,.. _tools-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,
-      :doc:`AMD SMI <amdsmi:index>`,26.0.2,26.0.0,25.5.1,25.5.1,25.4.2,25.3.0,24.7.1,24.7.1,24.7.1,24.7.1,24.6.3,24.6.3,24.6.3,24.6.2,24.5.1,24.5.1,24.5.1,24.4.1,23.4.2,23.4.2
-      :doc:`ROCm Data Center Tool <rdc:index>`,1.1.0,1.1.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0
-      :doc:`rocminfo <rocminfo:index>`,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
-      :doc:`ROCm SMI <rocm_smi_lib:index>`,7.8.0,7.8.0,7.7.0,7.5.0,7.5.0,7.5.0,7.4.0,7.4.0,7.4.0,7.4.0,7.3.0,7.3.0,7.3.0,7.3.0,7.2.0,7.2.0,7.0.0,7.0.0,6.0.2,6.0.0
-      :doc:`ROCm Validation Suite <rocmvalidationsuite:index>`,1.2.0,1.2.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.0.60204,1.0.60202,1.0.60201,1.0.60200,1.0.60105,1.0.60102,1.0.60101,1.0.60100,1.0.60002,1.0.60000
-      ,,,,,,,,,,,,,,,,,,,,
-      PERFORMANCE TOOLS,,,,,,,,,,,,,,,,,,,,
-      :doc:`ROCm Bandwidth Test <rocm_bandwidth_test:index>`,2.6.0,2.6.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0
-      :doc:`ROCm Compute Profiler <rocprofiler-compute:index>`,3.2.3,3.2.3,3.1.1,3.1.1,3.1.0,3.1.0,3.0.0,3.0.0,3.0.0,3.0.0,2.0.1,2.0.1,2.0.1,2.0.1,N/A,N/A,N/A,N/A,N/A,N/A
-      :doc:`ROCm Systems Profiler <rocprofiler-systems:index>`,1.1.1,1.1.0,1.0.2,1.0.2,1.0.1,1.0.0,0.1.2,0.1.1,0.1.0,0.1.0,1.11.2,1.11.2,1.11.2,1.11.2,N/A,N/A,N/A,N/A,N/A,N/A
-      :doc:`ROCProfiler <rocprofiler:index>`,2.0.70002,2.0.70000,2.0.60403,2.0.60402,2.0.60401,2.0.60400,2.0.60303,2.0.60302,2.0.60301,2.0.60300,2.0.60204,2.0.60202,2.0.60201,2.0.60200,2.0.60105,2.0.60102,2.0.60101,2.0.60100,2.0.60002,2.0.60000
-      :doc:`ROCprofiler-SDK <rocprofiler-sdk:index>`,1.0.0,1.0.0,0.6.0,0.6.0,0.6.0,0.6.0,0.5.0,0.5.0,0.5.0,0.5.0,0.4.0,0.4.0,0.4.0,0.4.0,N/A,N/A,N/A,N/A,N/A,N/A
-      :doc:`ROCTracer <roctracer:index>`,4.1.70002,4.1.70000,4.1.60403,4.1.60402,4.1.60401,4.1.60400,4.1.60303,4.1.60302,4.1.60301,4.1.60300,4.1.60204,4.1.60202,4.1.60201,4.1.60200,4.1.60105,4.1.60102,4.1.60101,4.1.60100,4.1.60002,4.1.60000
-      ,,,,,,,,,,,,,,,,,,,,
-      DEVELOPMENT TOOLS,,,,,,,,,,,,,,,,,,,,
-      :doc:`HIPIFY <hipify:index>`,20.0.0,20.0.0,19.0.0,19.0.0,19.0.0,19.0.0,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24455,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
-      :doc:`ROCm CMake <rocmcmakebuildtools:index>`,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.13.0,0.13.0,0.13.0,0.13.0,0.12.0,0.12.0,0.12.0,0.12.0,0.11.0,0.11.0
-      :doc:`ROCdbgapi <rocdbgapi:index>`,0.77.4,0.77.3,0.77.2,0.77.2,0.77.2,0.77.2,0.77.0,0.77.0,0.77.0,0.77.0,0.76.0,0.76.0,0.76.0,0.76.0,0.71.0,0.71.0,0.71.0,0.71.0,0.71.0,0.71.0
-      :doc:`ROCm Debugger (ROCgdb) <rocgdb:index>`,16.3.0,16.3.0,15.2.0,15.2.0,15.2.0,15.2.0,15.2.0,15.2.0,15.2.0,15.2.0,14.2.0,14.2.0,14.2.0,14.2.0,14.1.0,14.1.0,14.1.0,14.1.0,13.2.0,13.2.0
-      `rocprofiler-register <https://github.com/ROCm/rocprofiler-register>`_,0.5.0,0.5.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.3.0,0.3.0,0.3.0,0.3.0,N/A,N/A
-      :doc:`ROCr Debug Agent <rocr_debug_agent:index>`,2.1.0,2.1.0,2.0.4,2.0.4,2.0.4,2.0.4,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3
-      ,,,,,,,,,,,,,,,,,,,,
-      COMPILERS,.. _compilers-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,
-      `clang-ocl <https://github.com/ROCm/clang-ocl>`_,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,0.5.0,0.5.0,0.5.0,0.5.0,0.5.0,0.5.0
-      :doc:`hipCC <hipcc:index>`,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
-      `Flang <https://github.com/ROCm/flang>`_,20.0.0.25385,20.0.0.25314,19.0.0.25224,19.0.0.25224,19.0.0.25184,19.0.0.25133,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24455,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
-      :doc:`llvm-project <llvm-project:index>`,20.0.0.25385,20.0.0.25314,19.0.0.25224,19.0.0.25224,19.0.0.25184,19.0.0.25133,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24491,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
-      `OpenMP <https://github.com/ROCm/llvm-project/tree/amd-staging/openmp>`_,20.0.0.25385,20.0.0.25314,19.0.0.25224,19.0.0.25224,19.0.0.25184,19.0.0.25133,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24491,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
-      ,,,,,,,,,,,,,,,,,,,,
-      RUNTIMES,.. _runtime-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,
-      :doc:`AMD CLR <hip:understand/amd_clr>`,7.0.51831,7.0.51830,6.4.43484,6.4.43484,6.4.43483,6.4.43482,6.3.42134,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
-      :doc:`HIP <hip:index>`,7.0.51831,7.0.51830,6.4.43484,6.4.43484,6.4.43483,6.4.43482,6.3.42134,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
-      `OpenCL Runtime <https://github.com/ROCm/clr/tree/develop/opencl>`_,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0
-      :doc:`ROCr Runtime <rocr-runtime:index>`,1.18.0,1.18.0,1.15.0,1.15.0,1.15.0,1.15.0,1.14.0,1.14.0,1.14.0,1.14.0,1.14.0,1.14.0,1.14.0,1.13.0,1.13.0,1.13.0,1.13.0,1.13.0,1.12.0,1.12.0
+ROCm Version,7.2.0,7.1.1,7.1.0,7.0.2,7.0.1/7.0.0,6.4.3,6.4.2,6.4.1,6.4.0,6.3.3,6.3.2,6.3.1,6.3.0,6.2.4,6.2.2,6.2.1,6.2.0, 6.1.5, 6.1.2, 6.1.1, 6.1.0, 6.0.2, 6.0.0
+      :ref:`Operating systems & kernels <OS-kernel-versions>` [#os-compatibility-past-60]_,Ubuntu 24.04.3,Ubuntu 24.04.3,Ubuntu 24.04.3,Ubuntu 24.04.3,Ubuntu 24.04.3,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,"Ubuntu 24.04.1, 24.04","Ubuntu 24.04.1, 24.04","Ubuntu 24.04.1, 24.04",Ubuntu 24.04,,,,,,
+      ,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,"Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3, 22.04.2","Ubuntu 22.04.4, 22.04.3, 22.04.2"
+      ,,,,,,,,,,,,,,,,,,"Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5"
+      ,"RHEL 10.1, 10.0, 9.7, 9.6, 9.4","RHEL 10.1, 10.0, 9.7, 9.6, 9.4","RHEL 10.0, 9.6, 9.4","RHEL 10.0, 9.6, 9.4","RHEL 9.6, 9.4","RHEL 9.6, 9.4","RHEL 9.6, 9.4","RHEL 9.6, 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.4, 9.3","RHEL 9.4, 9.3","RHEL 9.4, 9.3","RHEL 9.4, 9.3","RHEL 9.4, 9.3, 9.2","RHEL 9.4, 9.3, 9.2","RHEL 9.4, 9.3, 9.2","RHEL 9.4, 9.3, 9.2","RHEL 9.3, 9.2","RHEL 9.3, 9.2"
+      ,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,"RHEL 8.10, 8.9","RHEL 8.10, 8.9","RHEL 8.10, 8.9","RHEL 8.10, 8.9","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8"
+      ,SLES 15 SP7,SLES 15 SP7,SLES 15 SP7,SLES 15 SP7,SLES 15 SP7,"SLES 15 SP7, SP6","SLES 15 SP7, SP6",SLES 15 SP6,SLES 15 SP6,"SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4"
+      ,,,,,,,,,,,,,,,,,,,CentOS 7.9,CentOS 7.9,CentOS 7.9,CentOS 7.9,CentOS 7.9
+      ,"Oracle Linux 10, 9, 8","Oracle Linux 10, 9, 8","Oracle Linux 10, 9, 8","Oracle Linux 10, 9, 8","Oracle Linux 9, 8","Oracle Linux 9, 8","Oracle Linux 9, 8","Oracle Linux 9, 8","Oracle Linux 9, 8",Oracle Linux 8.10,Oracle Linux 8.10,Oracle Linux 8.10,Oracle Linux 8.10,Oracle Linux 8.9,Oracle Linux 8.9,Oracle Linux 8.9,Oracle Linux 8.9,Oracle Linux 8.9,Oracle Linux 8.9,Oracle Linux 8.9,,,
+      ,"Debian 13, 12","Debian 13, 12","Debian 13, 12","Debian 13, 12",Debian 12,Debian 12,Debian 12,Debian 12,Debian 12,Debian 12,Debian 12,Debian 12,,,,,,,,,,,
+      ,,,,Azure Linux 3.0,Azure Linux 3.0,Azure Linux 3.0,Azure Linux 3.0,Azure Linux 3.0,Azure Linux 3.0,Azure Linux 3.0,Azure Linux 3.0,,,,,,,,,,,,
+      ,Rocky Linux 9,Rocky Linux 9,Rocky Linux 9,Rocky Linux 9,Rocky Linux 9,,,,,,,,,,,,,,,,,,
+      ,.. _architecture-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,
+      :doc:`Architecture <rocm-install-on-linux:reference/system-requirements>`,CDNA4,CDNA4,CDNA4,CDNA4,CDNA4,,,,,,,,,,,,,,,,,,
+      ,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3
+      ,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2
+      ,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA
+      ,RDNA4,RDNA4,RDNA4,RDNA4,RDNA4,RDNA4,RDNA4,RDNA4,,,,,,,,,,,,,,,
+      ,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3
+      ,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2
+      ,.. _gpu-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,
+      :doc:`GPU / LLVM target <rocm-install-on-linux:reference/system-requirements>`  [#gpu-compatibility-past-60]_,gfx950,gfx950,gfx950,gfx950,gfx950,,,,,,,,,,,,,,,,,,
+      ,gfx1201,gfx1201,gfx1201,gfx1201,gfx1201,gfx1201,gfx1201,gfx1201,,,,,,,,,,,,,,,
+      ,gfx1200,gfx1200,gfx1200,gfx1200,gfx1200,gfx1200,gfx1200,gfx1200,,,,,,,,,,,,,,,
+      ,gfx1101,gfx1101,gfx1101,gfx1101,gfx1101,gfx1101,gfx1101,gfx1101,,,,,,,,,,,,,,,
+      ,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100
+      ,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030
+      ,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942, gfx942, gfx942, gfx942, gfx942, gfx942, gfx942
+      ,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a
+      ,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908
+      ,,,,,,,,,,,,,,,,,,,,,,,
+      FRAMEWORK SUPPORT,.. _framework-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,
+      :doc:`PyTorch <../compatibility/ml-compatibility/pytorch-compatibility>`,"2.9.1, 2.8.0, 2.7.1","2.9, 2.8, 2.7","2.8, 2.7, 2.6","2.8, 2.7, 2.6","2.7, 2.6, 2.5","2.6, 2.5, 2.4, 2.3","2.6, 2.5, 2.4, 2.3","2.6, 2.5, 2.4, 2.3","2.6, 2.5, 2.4, 2.3","2.4, 2.3, 2.2, 1.13","2.4, 2.3, 2.2, 1.13","2.4, 2.3, 2.2, 1.13","2.4, 2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13"
+      :doc:`TensorFlow <../compatibility/ml-compatibility/tensorflow-compatibility>`,"2.20.0, 2.19.1, 2.18.1","2.20.0, 2.19.1, 2.18.1","2.20.0, 2.19.1, 2.18.1","2.19.1, 2.18.1, 2.17.1 [#tf-mi350-past-60]_","2.19.1, 2.18.1, 2.17.1 [#tf-mi350-past-60]_","2.18.1, 2.17.1, 2.16.2","2.18.1, 2.17.1, 2.16.2","2.18.1, 2.17.1, 2.16.2","2.18.1, 2.17.1, 2.16.2","2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.14.0, 2.13.1, 2.12.1","2.14.0, 2.13.1, 2.12.1"
+      :doc:`JAX <../compatibility/ml-compatibility/jax-compatibility>`,0.8.0,0.7.1,0.7.1,0.6.0,0.6.0,0.4.35,0.4.35,0.4.35,0.4.35,0.4.31,0.4.31,0.4.31,0.4.31,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26
+      :doc:`DGL <../compatibility/ml-compatibility/dgl-compatibility>` [#dgl_compat-past-60]_,N/A,N/A,N/A,N/A,2.4.0,2.4.0,N/A,N/A,2.4.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
+      `ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,1.23.2,1.23.1,1.22.0,1.22.0,1.22.0,1.20.0,1.20.0,1.20.0,1.20.0,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.14.1,1.14.1
+      ,,,,,,,,,,,,,,,,,,,,,,,
+      ,,,,,,,,,,,,,,,,,,,,,,,
+      THIRD PARTY COMMS,.. _thirdpartycomms-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,
+      `UCC <https://github.com/ROCm/ucc>`_,>=1.4.0,>=1.4.0,>=1.4.0,>=1.4.0,>=1.4.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.2.0,>=1.2.0
+      `UCX <https://github.com/ROCm/ucx>`_,>=1.17.0,>=1.17.0,>=1.17.0,>=1.17.0,>=1.17.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.14.1,>=1.14.1,>=1.14.1,>=1.14.1,>=1.14.1,>=1.14.1
+      ,,,,,,,,,,,,,,,,,,,,,,,
+      THIRD PARTY ALGORITHM,.. _thirdpartyalgorithm-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,
+      Thrust,2.8.5,2.8.5,2.8.5,2.6.0,2.6.0,2.5.0,2.5.0,2.5.0,2.5.0,2.3.2,2.3.2,2.3.2,2.3.2,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.1,2.0.1
+      CUB,2.8.5,2.8.5,2.8.5,2.6.0,2.6.0,2.5.0,2.5.0,2.5.0,2.5.0,2.3.2,2.3.2,2.3.2,2.3.2,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.1,2.0.1
+      ,,,,,,,,,,,,,,,,,,,,,,,
+     DRIVER & USER SPACE [#kfd_support-past-60]_,.. _kfd-userspace-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,
+      :doc:`AMD GPU Driver <rocm-install-on-linux:reference/user-kernel-space-compat-matrix>`,"30.30.0, 30.20.1, 30.20.0 [#mi325x_KVM-past-60]_, 30.10.2, 30.10.1 [#driver_patch-past-60]_, 30.10, 6.4.x","30.20.1, 30.20.0 [#mi325x_KVM-past-60]_, 30.10.2, 30.10.1 [#driver_patch-past-60]_, 30.10, 6.4.x","30.20.0 [#mi325x_KVM-past-60]_, 30.10.2, 30.10.1 [#driver_patch-past-60]_, 30.10, 6.4.x","30.10.2, 30.10.1 [#driver_patch-past-60]_, 30.10, 6.4.x, 6.3.x","30.10.1 [#driver_patch-past-60]_, 30.10, 6.4.x, 6.3.x, 6.2.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.2.x, 6.1.x, 6.0.x, 5.7.x, 5.6.x","6.2.x, 6.1.x, 6.0.x, 5.7.x, 5.6.x"
+      ,,,,,,,,,,,,,,,,,,,,,,,
+      ML & COMPUTER VISION,.. _mllibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,
+      :doc:`Composable Kernel <composable_kernel:index>`,1.2.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0
+      :doc:`MIGraphX <amdmigraphx:index>`,2.15.0,2.14.0,2.14.0,2.13.0,2.13.0,2.12.0,2.12.0,2.12.0,2.12.0,2.11.0,2.11.0,2.11.0,2.11.0,2.10.0,2.10.0,2.10.0,2.10.0,2.9.0,2.9.0,2.9.0,2.9.0,2.8.0,2.8.0
+      :doc:`MIOpen <miopen:index>`,3.5.1,3.5.1,3.5.1,3.5.0,3.5.0,3.4.0,3.4.0,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.0,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
+      :doc:`MIVisionX <mivisionx:index>`,3.5.0,3.4.0,3.4.0,3.3.0,3.3.0,3.2.0,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0,3.0.0,3.0.0,2.5.0,2.5.0,2.5.0,2.5.0,2.5.0,2.5.0
+      :doc:`rocAL <rocal:index>`,2.5.0,2.4.0,2.4.0,2.3.0,2.3.0,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.0,2.0.0,2.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
+      :doc:`rocDecode <rocdecode:index>`,1.5.0,1.4.0,1.4.0,1.0.0,1.0.0,0.10.0,0.10.0,0.10.0,0.10.0,0.8.0,0.8.0,0.8.0,0.8.0,0.6.0,0.6.0,0.6.0,0.6.0,0.6.0,0.6.0,0.5.0,0.5.0,N/A,N/A
+      :doc:`rocJPEG <rocjpeg:index>`,1.3.0,1.2.0,1.2.0,1.1.0,1.1.0,0.8.0,0.8.0,0.8.0,0.8.0,0.6.0,0.6.0,0.6.0,0.6.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
+      :doc:`rocPyDecode <rocpydecode:index>`,0.8.0,0.7.0,0.7.0,0.6.0,0.6.0,0.3.1,0.3.1,0.3.1,0.3.1,0.2.0,0.2.0,0.2.0,0.2.0,0.1.0,0.1.0,0.1.0,0.1.0,N/A,N/A,N/A,N/A,N/A,N/A
+      :doc:`RPP <rpp:index>`,2.2.0,2.1.0,2.1.0,2.0.0,2.0.0,1.9.10,1.9.10,1.9.10,1.9.10,1.9.1,1.9.1,1.9.1,1.9.1,1.8.0,1.8.0,1.8.0,1.8.0,1.5.0,1.5.0,1.5.0,1.5.0,1.4.0,1.4.0
+      ,,,,,,,,,,,,,,,,,,,,,,,
+      COMMUNICATION,.. _commlibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,
+      :doc:`RCCL <rccl:index>`,2.27.7,2.27.7,2.27.7,2.26.6,2.26.6,2.22.3,2.22.3,2.22.3,2.22.3,2.21.5,2.21.5,2.21.5,2.21.5,2.20.5,2.20.5,2.20.5,2.20.5,2.18.6,2.18.6,2.18.6,2.18.6,2.18.3,2.18.3
+      :doc:`rocSHMEM <rocshmem:index>`,3.2.0,3.1.0,3.0.0,3.0.0,3.0.0,2.0.1,2.0.1,2.0.0,2.0.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
+      ,,,,,,,,,,,,,,,,,,,,,,,
+      MATH LIBS,.. _mathlibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,
+      `half <https://github.com/ROCm/half>`_ ,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0
+      :doc:`hipBLAS <hipblas:index>`,3.2.0,3.1.0,3.1.0,3.0.2,3.0.0,2.4.0,2.4.0,2.4.0,2.4.0,2.3.0,2.3.0,2.3.0,2.3.0,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.0,2.0.0
+      :doc:`hipBLASLt <hipblaslt:index>`,1.2.1,1.1.0,1.1.0,1.0.0,1.0.0,0.12.1,0.12.1,0.12.1,0.12.0,0.10.0,0.10.0,0.10.0,0.10.0,0.8.0,0.8.0,0.8.0,0.8.0,0.7.0,0.7.0,0.7.0,0.7.0,0.6.0,0.6.0
+      :doc:`hipFFT <hipfft:index>`,1.0.22,1.0.21,1.0.21,1.0.20,1.0.20,1.0.18,1.0.18,1.0.18,1.0.18,1.0.17,1.0.17,1.0.17,1.0.17,1.0.16,1.0.15,1.0.15,1.0.14,1.0.14,1.0.14,1.0.14,1.0.14,1.0.13,1.0.13
+      :doc:`hipfort <hipfort:index>`,0.7.1,0.7.1,0.7.1,0.7.0,0.7.0,0.6.0,0.6.0,0.6.0,0.6.0,0.5.1,0.5.1,0.5.0,0.5.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0
+      :doc:`hipRAND <hiprand:index>`,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0,2.12.0,2.12.0,2.12.0,2.12.0,2.11.1,2.11.1,2.11.1,2.11.0,2.11.1,2.11.0,2.11.0,2.11.0,2.10.16,2.10.16,2.10.16,2.10.16,2.10.16,2.10.16
+      :doc:`hipSOLVER <hipsolver:index>`,3.2.0,3.1.0,3.1.0,3.0.0,3.0.0,2.4.0,2.4.0,2.4.0,2.4.0,2.3.0,2.3.0,2.3.0,2.3.0,2.2.0,2.2.0,2.2.0,2.2.0,2.1.1,2.1.1,2.1.1,2.1.0,2.0.0,2.0.0
+      :doc:`hipSPARSE <hipsparse:index>`,4.2.0,4.1.0,4.1.0,4.0.1,4.0.1,3.2.0,3.2.0,3.2.0,3.2.0,3.1.2,3.1.2,3.1.2,3.1.2,3.1.1,3.1.1,3.1.1,3.1.1,3.0.1,3.0.1,3.0.1,3.0.1,3.0.0,3.0.0
+      :doc:`hipSPARSELt <hipsparselt:index>`,0.2.6,0.2.5,0.2.5,0.2.4,0.2.4,0.2.3,0.2.3,0.2.3,0.2.3,0.2.2,0.2.2,0.2.2,0.2.2,0.2.1,0.2.1,0.2.1,0.2.1,0.2.0,0.2.0,0.1.0,0.1.0,0.1.0,0.1.0
+      :doc:`rocALUTION <rocalution:index>`,4.1.0,4.0.1,4.0.1,4.0.0,4.0.0,3.2.3,3.2.3,3.2.3,3.2.2,3.2.1,3.2.1,3.2.1,3.2.1,3.2.1,3.2.0,3.2.0,3.2.0,3.1.1,3.1.1,3.1.1,3.1.1,3.0.3,3.0.3
+      :doc:`rocBLAS <rocblas:index>`,5.2.0,5.1.1,5.1.0,5.0.2,5.0.0,4.4.1,4.4.1,4.4.0,4.4.0,4.3.0,4.3.0,4.3.0,4.3.0,4.2.4,4.2.1,4.2.1,4.2.0,4.1.2,4.1.2,4.1.0,4.1.0,4.0.0,4.0.0
+      :doc:`rocFFT <rocfft:index>`,1.0.36,1.0.35,1.0.35,1.0.34,1.0.34,1.0.32,1.0.32,1.0.32,1.0.32,1.0.31,1.0.31,1.0.31,1.0.31,1.0.30,1.0.29,1.0.29,1.0.28,1.0.27,1.0.27,1.0.27,1.0.26,1.0.25,1.0.23
+      :doc:`rocRAND <rocrand:index>`,4.2.0,4.1.0,4.1.0,4.0.0,4.0.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.0,3.2.0,3.2.0,3.2.0,3.1.1,3.1.0,3.1.0,3.1.0,3.0.1,3.0.1,3.0.1,3.0.1,3.0.0,2.10.17
+      :doc:`rocSOLVER <rocsolver:index>`,3.32.0,3.31.0,3.31.0,3.30.1,3.30.0,3.28.2,3.28.2,3.28.0,3.28.0,3.27.0,3.27.0,3.27.0,3.27.0,3.26.2,3.26.0,3.26.0,3.26.0,3.25.0,3.25.0,3.25.0,3.25.0,3.24.0,3.24.0
+      :doc:`rocSPARSE <rocsparse:index>`,4.2.0,4.1.0,4.1.0,4.0.2,4.0.2,3.4.0,3.4.0,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.1,3.2.0,3.2.0,3.2.0,3.1.2,3.1.2,3.1.2,3.1.2,3.0.2,3.0.2
+      :doc:`rocWMMA <rocwmma:index>`,2.2.0,2.1.0,2.0.0,2.0.0,2.0.0,1.7.0,1.7.0,1.7.0,1.7.0,1.6.0,1.6.0,1.6.0,1.6.0,1.5.0,1.5.0,1.5.0,1.5.0,1.4.0,1.4.0,1.4.0,1.4.0,1.3.0,1.3.0
+      :doc:`Tensile <tensile:src/index>`,4.45.0,4.44.0,4.44.0,4.44.0,4.44.0,4.43.0,4.43.0,4.43.0,4.43.0,4.42.0,4.42.0,4.42.0,4.42.0,4.41.0,4.41.0,4.41.0,4.41.0,4.40.0,4.40.0,4.40.0,4.40.0,4.39.0,4.39.0
+      ,,,,,,,,,,,,,,,,,,,,,,,
+      PRIMITIVES,.. _primitivelibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,
+      :doc:`hipCUB <hipcub:index>`,4.2.0,4.1.0,4.1.0,4.0.0,4.0.0,3.4.0,3.4.0,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.1,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
+      :doc:`hipTensor <hiptensor:index>`,2.2.0,2.0.0,2.0.0,2.0.0,2.0.0,1.5.0,1.5.0,1.5.0,1.5.0,1.4.0,1.4.0,1.4.0,1.4.0,1.3.0,1.3.0,1.3.0,1.3.0,1.2.0,1.2.0,1.2.0,1.2.0,1.1.0,1.1.0
+      :doc:`rocPRIM <rocprim:index>`,4.2.0,4.1.0,4.1.0,4.0.1,4.0.0,3.4.1,3.4.1,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.2,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
+      :doc:`rocThrust <rocthrust:index>`,4.2.0,4.1.0,4.1.0,4.0.0,4.0.0,3.3.0,3.3.0,3.3.0,3.3.0,3.3.0,3.3.0,3.3.0,3.3.0,3.1.1,3.1.0,3.1.0,3.0.1,3.0.1,3.0.1,3.0.1,3.0.1,3.0.0,3.0.0
+      ,,,,,,,,,,,,,,,,,,,,,,,
+      SUPPORT LIBS,,,,,,,,,,,,,,,,,,,,,,,
+      `hipother <https://github.com/ROCm/hipother>`_,7.2.26015,7.1.52802,7.1.25424,7.0.51831,7.0.51830,6.4.43483,6.4.43483,6.4.43483,6.4.43482,6.3.42134,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
+      `rocm-core <https://github.com/ROCm/rocm-core>`_,7.2.0,7.1.1,7.1.0,7.0.2,7.0.1/7.0.0,6.4.3,6.4.2,6.4.1,6.4.0,6.3.3,6.3.2,6.3.1,6.3.0,6.2.4,6.2.2,6.2.1,6.2.0,6.1.5,6.1.2,6.1.1,6.1.0,6.0.2,6.0.0
+      `ROCT-Thunk-Interface <https://github.com/ROCm/ROCT-Thunk-Interface>`_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,20240607.5.7,20240607.5.7,20240607.4.05,20240607.1.4246,20240125.5.08,20240125.5.08,20240125.5.08,20240125.3.30,20231016.2.245,20231016.2.245
+      ,,,,,,,,,,,,,,,,,,,,,,,
+      SYSTEM MGMT TOOLS,.. _tools-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,
+      :doc:`AMD SMI <amdsmi:index>`,26.2.1,26.2.0,26.1.0,26.0.2,26.0.0,25.5.1,25.5.1,25.4.2,25.3.0,24.7.1,24.7.1,24.7.1,24.7.1,24.6.3,24.6.3,24.6.3,24.6.2,24.5.1,24.5.1,24.5.1,24.4.1,23.4.2,23.4.2
+      :doc:`ROCm Data Center Tool <rdc:index>`,1.2.0,1.2.0,1.2.0,1.1.0,1.1.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0
+      :doc:`rocminfo <rocminfo:index>`,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
+      :doc:`ROCm SMI <rocm_smi_lib:index>`,7.8.0,7.8.0,7.8.0,7.8.0,7.8.0,7.7.0,7.5.0,7.5.0,7.5.0,7.4.0,7.4.0,7.4.0,7.4.0,7.3.0,7.3.0,7.3.0,7.3.0,7.2.0,7.2.0,7.0.0,7.0.0,6.0.2,6.0.0
+      :doc:`ROCm Validation Suite <rocmvalidationsuite:index>`,1.3.0,1.3.0,1.2.0,1.2.0,1.2.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.0.60204,1.0.60202,1.0.60201,1.0.60200,1.0.60105,1.0.60102,1.0.60101,1.0.60100,1.0.60002,1.0.60000
+      ,,,,,,,,,,,,,,,,,,,,,,,
+      PERFORMANCE TOOLS,,,,,,,,,,,,,,,,,,,,,,,
+      :doc:`ROCm Bandwidth Test <rocm_bandwidth_test:index>`,2.6.0,2.6.0,2.6.0,2.6.0,2.6.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0
+      :doc:`ROCm Compute Profiler <rocprofiler-compute:index>`,3.4.0,3.3.1,3.3.0,3.2.3,3.2.3,3.1.1,3.1.1,3.1.0,3.1.0,3.0.0,3.0.0,3.0.0,3.0.0,2.0.1,2.0.1,2.0.1,2.0.1,N/A,N/A,N/A,N/A,N/A,N/A
+      :doc:`ROCm Systems Profiler <rocprofiler-systems:index>`,1.3.0,1.2.1,1.2.0,1.1.1,1.1.0,1.0.2,1.0.2,1.0.1,1.0.0,0.1.2,0.1.1,0.1.0,0.1.0,1.11.2,1.11.2,1.11.2,1.11.2,N/A,N/A,N/A,N/A,N/A,N/A
+      :doc:`ROCProfiler <rocprofiler:index>`,2.0.70200,2.0.70101,2.0.70100,2.0.70002,2.0.70000,2.0.60403,2.0.60402,2.0.60401,2.0.60400,2.0.60303,2.0.60302,2.0.60301,2.0.60300,2.0.60204,2.0.60202,2.0.60201,2.0.60200,2.0.60105,2.0.60102,2.0.60101,2.0.60100,2.0.60002,2.0.60000
+      :doc:`ROCprofiler-SDK <rocprofiler-sdk:index>`,1.1.0,1.0.0,1.0.0,1.0.0,1.0.0,0.6.0,0.6.0,0.6.0,0.6.0,0.5.0,0.5.0,0.5.0,0.5.0,0.4.0,0.4.0,0.4.0,0.4.0,N/A,N/A,N/A,N/A,N/A,N/A
+      :doc:`ROCTracer <roctracer:index>`,4.1.70200,4.1.70101,4.1.70100,4.1.70002,4.1.70000,4.1.60403,4.1.60402,4.1.60401,4.1.60400,4.1.60303,4.1.60302,4.1.60301,4.1.60300,4.1.60204,4.1.60202,4.1.60201,4.1.60200,4.1.60105,4.1.60102,4.1.60101,4.1.60100,4.1.60002,4.1.60000
+      ,,,,,,,,,,,,,,,,,,,,,,,
+      DEVELOPMENT TOOLS,,,,,,,,,,,,,,,,,,,,,,,
+      :doc:`HIPIFY <hipify:index>`,22.0.0,20.0.0,20.0.0,20.0.0,20.0.0,19.0.0,19.0.0,19.0.0,19.0.0,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24455,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
+      :doc:`ROCm CMake <rocmcmakebuildtools:index>`,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.13.0,0.13.0,0.13.0,0.13.0,0.12.0,0.12.0,0.12.0,0.12.0,0.11.0,0.11.0
+      :doc:`ROCdbgapi <rocdbgapi:index>`,0.77.4,0.77.4,0.77.4,0.77.4,0.77.3,0.77.2,0.77.2,0.77.2,0.77.2,0.77.0,0.77.0,0.77.0,0.77.0,0.76.0,0.76.0,0.76.0,0.76.0,0.71.0,0.71.0,0.71.0,0.71.0,0.71.0,0.71.0
+      :doc:`ROCm Debugger (ROCgdb) <rocgdb:index>`,16.3.0,16.3.0,16.3.0,16.3.0,16.3.0,15.2.0,15.2.0,15.2.0,15.2.0,15.2.0,15.2.0,15.2.0,15.2.0,14.2.0,14.2.0,14.2.0,14.2.0,14.1.0,14.1.0,14.1.0,14.1.0,13.2.0,13.2.0
+      `rocprofiler-register <https://github.com/ROCm/rocprofiler-register>`_,0.5.0,0.5.0,0.5.0,0.5.0,0.5.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.3.0,0.3.0,0.3.0,0.3.0,N/A,N/A
+      :doc:`ROCr Debug Agent <rocr_debug_agent:index>`,2.1.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.4,2.0.4,2.0.4,2.0.4,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3
+      ,,,,,,,,,,,,,,,,,,,,,,,
+      COMPILERS,.. _compilers-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,
+      `clang-ocl <https://github.com/ROCm/clang-ocl>`_,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,0.5.0,0.5.0,0.5.0,0.5.0,0.5.0,0.5.0
+      :doc:`hipCC <hipcc:index>`,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
+      `Flang <https://github.com/ROCm/flang>`_,22.0.0.26014,20.0.025444,20.0.025425,20.0.0.25385,20.0.0.25314,19.0.0.25224,19.0.0.25224,19.0.0.25184,19.0.0.25133,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24455,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
+      :doc:`llvm-project <llvm-project:index>`,22.0.0.26014,20.0.025444,20.0.025425,20.0.0.25385,20.0.0.25314,19.0.0.25224,19.0.0.25224,19.0.0.25184,19.0.0.25133,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24491,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
+      `OpenMP <https://github.com/ROCm/llvm-project/tree/amd-staging/openmp>`_,22.0.0.26014,20.0.025444,20.0.025425,20.0.0.25385,20.0.0.25314,19.0.0.25224,19.0.0.25224,19.0.0.25184,19.0.0.25133,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24491,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
+      ,,,,,,,,,,,,,,,,,,,,,,,
+      RUNTIMES,.. _runtime-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,
+      :doc:`AMD CLR <hip:understand/amd_clr>`,7.2.26015,7.1.52802,7.1.25424,7.0.51831,7.0.51830,6.4.43484,6.4.43484,6.4.43483,6.4.43482,6.3.42134,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
+      :doc:`HIP <hip:index>`,7.2.26015,7.1.52802,7.1.25424,7.0.51831,7.0.51830,6.4.43484,6.4.43484,6.4.43483,6.4.43482,6.3.42134,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
+      `OpenCL Runtime <https://github.com/ROCm/clr/tree/develop/opencl>`_,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0
+      :doc:`ROCr Runtime <rocr-runtime:index>`,1.18.0,1.18.0,1.18.0,1.18.0,1.18.0,1.15.0,1.15.0,1.15.0,1.15.0,1.14.0,1.14.0,1.14.0,1.14.0,1.14.0,1.14.0,1.14.0,1.13.0,1.13.0,1.13.0,1.13.0,1.13.0,1.12.0,1.12.0
@@ -12,7 +12,7 @@ You can also refer to the :ref:`past versions of ROCm compatibility matrix<past-

 GPUs listed in the following table support compute workloads (no display
 information or graphics). If you’re using ROCm with AMD Radeon GPUs or Ryzen APUs for graphics
-workloads, see the :docs:`Use ROCm on Radeon and Ryzen <radeon:index.html>` to verify
+workloads, see the :doc:`Use ROCm on Radeon and Ryzen <radeon:index>` to verify
 compatibility and system requirements.

 .. |br| raw:: html
@@ -22,18 +22,18 @@ compatibility and system requirements.
 .. container:: format-big-table

  .. csv-table::
-      :header: "ROCm Version", "7.0.2", "7.0.1/7.0.0", "6.4.0"
+      :header: "ROCm Version", "7.2.0", "7.1.1", "6.4.0"
      :stub-columns: 1

-      :ref:`Operating systems & kernels <OS-kernel-versions>`,Ubuntu 24.04.3,Ubuntu 24.04.3,Ubuntu 24.04.2
+      :ref:`Operating systems & kernels <OS-kernel-versions>` [#os-compatibility]_,Ubuntu 24.04.3,Ubuntu 24.04.3,Ubuntu 24.04.2
      ,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5
-      ,"RHEL 10.0 [#rhel-10-702]_, 9.6 [#rhel-10-702]_, 9.4 [#rhel-94-702]_","RHEL 9.6 [#rhel-10-702]_, 9.4 [#rhel-94-702]_","RHEL 9.5, 9.4"
-      ,RHEL 8.10 [#rhel-700]_,RHEL 8.10 [#rhel-700]_,RHEL 8.10
-      ,SLES 15 SP7 [#sles-db-700]_,SLES 15 SP7 [#sles-db-700]_,SLES 15 SP6
-      ,"Oracle Linux 10, 9, 8 [#ol-700-mi300x]_","Oracle Linux 9, 8 [#ol-700-mi300x]_","Oracle Linux 9, 8 [#ol-mi300x]_"
-      ,"Debian 13 [#db-mi300x]_, 12 [#sles-db-700]_",Debian 12 [#sles-db-700]_,Debian 12 [#single-node]_
-      ,Azure Linux 3.0 [#az-mi300x]_,Azure Linux 3.0 [#az-mi300x]_,Azure Linux 3.0 [#az-mi300x]_
-      ,Rocky Linux 9 [#rl-700]_,Rocky Linux 9 [#rl-700]_,
+      ,"RHEL 10.1, 10.0, 9.7, 9.6, 9.4","RHEL 10.1, 10.0, 9.7, 9.6, 9.4","RHEL 9.5, 9.4"
+      ,RHEL 8.10,RHEL 8.10,RHEL 8.10
+      ,SLES 15 SP7,SLES 15 SP7,SLES 15 SP6
+      ,"Oracle Linux 10, 9, 8","Oracle Linux 10, 9, 8","Oracle Linux 9, 8"
+      ,"Debian 13, 12","Debian 13, 12",Debian 12
+      ,,,Azure Linux 3.0
+      ,Rocky Linux 9,Rocky Linux 9,
      ,.. _architecture-support-compatibility-matrix:,,
      :doc:`Architecture <rocm-install-on-linux:reference/system-requirements>`,CDNA4,CDNA4,
      ,CDNA3,CDNA3,CDNA3
@@ -43,99 +43,98 @@ compatibility and system requirements.
      ,RDNA3,RDNA3,RDNA3
      ,RDNA2,RDNA2,RDNA2
      ,.. _gpu-support-compatibility-matrix:,,
-      :doc:`GPU / LLVM target <rocm-install-on-linux:reference/system-requirements>`,gfx950 [#mi350x-os]_,gfx950 [#mi350x-os]_,
-      ,gfx1201 [#RDNA-OS-700]_,gfx1201 [#RDNA-OS-700]_,
-      ,gfx1200 [#RDNA-OS-700]_,gfx1200 [#RDNA-OS-700]_,
-      ,gfx1101 [#RDNA-OS-700]_ [#rd-v710]_,gfx1101 [#RDNA-OS-700]_ [#rd-v710]_,
-      ,gfx1100 [#RDNA-OS-700]_,gfx1100 [#RDNA-OS-700]_,gfx1100
-      ,gfx1030 [#RDNA-OS-700]_ [#rd-v620]_,gfx1030 [#RDNA-OS-700]_ [#rd-v620]_,gfx1030
-      ,gfx942 [#mi325x-os]_ [#mi300x-os]_ [#mi300A-os]_,gfx942 [#mi325x-os]_ [#mi300x-os]_ [#mi300A-os]_,gfx942
-      ,gfx90a [#mi200x-os]_,gfx90a [#mi200x-os]_,gfx90a
-      ,gfx908 [#mi100-os]_,gfx908 [#mi100-os]_,gfx908
+      :doc:`GPU / LLVM target <rocm-install-on-linux:reference/system-requirements>`  [#gpu-compatibility]_,gfx950,gfx950,
+      ,gfx1201,gfx1201,
+      ,gfx1200,gfx1200,
+      ,gfx1101,gfx1101,
+      ,gfx1100,gfx1100,gfx1100
+      ,gfx1030,gfx1030,gfx1030
+      ,gfx942,gfx942,gfx942
+      ,gfx90a,gfx90a,gfx90a
+      ,gfx908,gfx908,gfx908
      ,,,
      FRAMEWORK SUPPORT,.. _framework-support-compatibility-matrix:,,
-      :doc:`PyTorch <../compatibility/ml-compatibility/pytorch-compatibility>`,"2.8, 2.7, 2.6","2.7, 2.6, 2.5","2.6, 2.5, 2.4, 2.3"
-      :doc:`TensorFlow <../compatibility/ml-compatibility/tensorflow-compatibility>`,"2.19.1, 2.18.1, 2.17.1 [#tf-mi350]_","2.19.1, 2.18.1, 2.17.1 [#tf-mi350]_","2.18.1, 2.17.1, 2.16.2"
-      :doc:`JAX <../compatibility/ml-compatibility/jax-compatibility>`,0.6.0,0.6.0,0.4.35
+      :doc:`PyTorch <../compatibility/ml-compatibility/pytorch-compatibility>`,"2.9.1, 2.8.0, 2.7.1","2.9, 2.8, 2.7","2.6, 2.5, 2.4, 2.3"
+      :doc:`TensorFlow <../compatibility/ml-compatibility/tensorflow-compatibility>`,"2.20.0, 2.19.1, 2.18.1","2.20.0, 2.19.1, 2.18.1","2.18.1, 2.17.1, 2.16.2"
+      :doc:`JAX <../compatibility/ml-compatibility/jax-compatibility>`,0.8.0,0.7.1,0.4.35
      :doc:`DGL <../compatibility/ml-compatibility/dgl-compatibility>` [#dgl_compat]_,N/A,N/A,2.4.0
-      :doc:`llama.cpp <../compatibility/ml-compatibility/llama-cpp-compatibility>` [#llama-cpp_compat]_,N/A,b6356,b5997
-      `ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,1.22.0,1.22.0,1.20.0
+      `ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,1.23.2,1.23.1,1.20.0
      ,,,
      THIRD PARTY COMMS,.. _thirdpartycomms-support-compatibility-matrix:,,
      `UCC <https://github.com/ROCm/ucc>`_,>=1.4.0,>=1.4.0,>=1.3.0
      `UCX <https://github.com/ROCm/ucx>`_,>=1.17.0,>=1.17.0,>=1.15.0
      ,,,
      THIRD PARTY ALGORITHM,.. _thirdpartyalgorithm-support-compatibility-matrix:,,
-      Thrust,2.6.0,2.6.0,2.5.0
-      CUB,2.6.0,2.6.0,2.5.0
+      Thrust,2.8.5,2.8.5,2.5.0
+      CUB,2.8.5,2.8.5,2.5.0
      ,,,
      DRIVER & USER SPACE [#kfd_support]_,.. _kfd-userspace-support-compatibility-matrix:,,
-      :doc:`AMD GPU Driver <rocm-install-on-linux:reference/user-kernel-space-compat-matrix>`,"30.10.2, 30.10.1 [#driver_patch]_, |br| 30.10, 6.4.x, 6.3.x","30.10.1 [#driver_patch]_, 30.10, |br| 6.4.x, 6.3.x, 6.2.x","6.4.x, 6.3.x, 6.2.x, 6.1.x"
+      :doc:`AMD GPU Driver <rocm-install-on-linux:reference/user-kernel-space-compat-matrix>`,"30.30.0, 30.20.1, 30.20.0 [#mi325x_KVM]_, |br| 30.10.2, 30.10.1 [#driver_patch]_, |br| 30.10, 6.4.x","30.20.1, 30.20.0 [#mi325x_KVM]_, |br| 30.10.2, 30.10.1 [#driver_patch]_, |br| 30.10, 6.4.x","6.4.x, 6.3.x, 6.2.x, 6.1.x"
      ,,,
      ML & COMPUTER VISION,.. _mllibs-support-compatibility-matrix:,,
-      :doc:`Composable Kernel <composable_kernel:index>`,1.1.0,1.1.0,1.1.0
-      :doc:`MIGraphX <amdmigraphx:index>`,2.13.0,2.13.0,2.12.0
-      :doc:`MIOpen <miopen:index>`,3.5.0,3.5.0,3.4.0
-      :doc:`MIVisionX <mivisionx:index>`,3.3.0,3.3.0,3.2.0
-      :doc:`rocAL <rocal:index>`,2.3.0,2.3.0,2.2.0
-      :doc:`rocDecode <rocdecode:index>`,1.0.0,1.0.0,0.10.0
-      :doc:`rocJPEG <rocjpeg:index>`,1.1.0,1.1.0,0.8.0
-      :doc:`rocPyDecode <rocpydecode:index>`,0.6.0,0.6.0,0.3.1
-      :doc:`RPP <rpp:index>`,2.0.0,2.0.0,1.9.10
+      :doc:`Composable Kernel <composable_kernel:index>`,1.2.0,1.1.0,1.1.0
+      :doc:`MIGraphX <amdmigraphx:index>`,2.15.0,2.14.0,2.12.0
+      :doc:`MIOpen <miopen:index>`,3.5.1,3.5.1,3.4.0
+      :doc:`MIVisionX <mivisionx:index>`,3.5.0,3.4.0,3.2.0
+      :doc:`rocAL <rocal:index>`,2.5.0,2.4.0,2.2.0
+      :doc:`rocDecode <rocdecode:index>`,1.5.0,1.4.0,0.10.0
+      :doc:`rocJPEG <rocjpeg:index>`,1.3.0,1.2.0,0.8.0
+      :doc:`rocPyDecode <rocpydecode:index>`,0.8.0,0.7.0,0.3.1
+      :doc:`RPP <rpp:index>`,2.2.0,2.1.0,1.9.10
      ,,,
      COMMUNICATION,.. _commlibs-support-compatibility-matrix:,,
-      :doc:`RCCL <rccl:index>`,2.26.6,2.26.6,2.22.3
-      :doc:`rocSHMEM <rocshmem:index>`,3.0.0,3.0.0,2.0.0
+      :doc:`RCCL <rccl:index>`,2.27.7,2.27.7,2.22.3
+      :doc:`rocSHMEM <rocshmem:index>`,3.2.0,3.1.0,2.0.0
      ,,,
      MATH LIBS,.. _mathlibs-support-compatibility-matrix:,,
      `half <https://github.com/ROCm/half>`_ ,1.12.0,1.12.0,1.12.0
-      :doc:`hipBLAS <hipblas:index>`,3.0.2,3.0.0,2.4.0
-      :doc:`hipBLASLt <hipblaslt:index>`,1.0.0,1.0.0,0.12.0
-      :doc:`hipFFT <hipfft:index>`,1.0.20,1.0.20,1.0.18
-      :doc:`hipfort <hipfort:index>`,0.7.0,0.7.0,0.6.0
-      :doc:`hipRAND <hiprand:index>`,3.0.0,3.0.0,2.12.0
-      :doc:`hipSOLVER <hipsolver:index>`,3.0.0,3.0.0,2.4.0
-      :doc:`hipSPARSE <hipsparse:index>`,4.0.1,4.0.1,3.2.0
-      :doc:`hipSPARSELt <hipsparselt:index>`,0.2.4,0.2.4,0.2.3
-      :doc:`rocALUTION <rocalution:index>`,4.0.0,4.0.0,3.2.2
-      :doc:`rocBLAS <rocblas:index>`,5.0.2,5.0.0,4.4.0
-      :doc:`rocFFT <rocfft:index>`,1.0.34,1.0.34,1.0.32
-      :doc:`rocRAND <rocrand:index>`,4.0.0,4.0.0,3.3.0
-      :doc:`rocSOLVER <rocsolver:index>`,3.30.1,3.30.0,3.28.0
-      :doc:`rocSPARSE <rocsparse:index>`,4.0.2,4.0.2,3.4.0
-      :doc:`rocWMMA <rocwmma:index>`,2.0.0,2.0.0,1.7.0
-      :doc:`Tensile <tensile:src/index>`,4.44.0,4.44.0,4.43.0
+      :doc:`hipBLAS <hipblas:index>`,3.2.0,3.1.0,2.4.0
+      :doc:`hipBLASLt <hipblaslt:index>`,1.2.1,1.1.0,0.12.0
+      :doc:`hipFFT <hipfft:index>`,1.0.22,1.0.21,1.0.18
+      :doc:`hipfort <hipfort:index>`,0.7.1,0.7.1,0.6.0
+      :doc:`hipRAND <hiprand:index>`,3.1.0,3.1.0,2.12.0
+      :doc:`hipSOLVER <hipsolver:index>`,3.2.0,3.1.0,2.4.0
+      :doc:`hipSPARSE <hipsparse:index>`,4.2.0,4.1.0,3.2.0
+      :doc:`hipSPARSELt <hipsparselt:index>`,0.2.6,0.2.5,0.2.3
+      :doc:`rocALUTION <rocalution:index>`,4.1.0,4.0.1,3.2.2
+      :doc:`rocBLAS <rocblas:index>`,5.2.0,5.1.1,4.4.0
+      :doc:`rocFFT <rocfft:index>`,1.0.36,1.0.35,1.0.32
+      :doc:`rocRAND <rocrand:index>`,4.2.0,4.1.0,3.3.0
+      :doc:`rocSOLVER <rocsolver:index>`,3.32.0,3.31.0,3.28.0
+      :doc:`rocSPARSE <rocsparse:index>`,4.2.0,4.1.0,3.4.0
+      :doc:`rocWMMA <rocwmma:index>`,2.2.0,2.1.0,1.7.0
+      :doc:`Tensile <tensile:src/index>`,4.45.0,4.44.0,4.43.0
      ,,,
      PRIMITIVES,.. _primitivelibs-support-compatibility-matrix:,,
-      :doc:`hipCUB <hipcub:index>`,4.0.0,4.0.0,3.4.0
-      :doc:`hipTensor <hiptensor:index>`,2.0.0,2.0.0,1.5.0
-      :doc:`rocPRIM <rocprim:index>`,4.0.1,4.0.0,3.4.0
-      :doc:`rocThrust <rocthrust:index>`,4.0.0,4.0.0,3.3.0
+      :doc:`hipCUB <hipcub:index>`,4.2.0,4.1.0,3.4.0
+      :doc:`hipTensor <hiptensor:index>`,2.2.0,2.0.0,1.5.0
+      :doc:`rocPRIM <rocprim:index>`,4.2.0,4.1.0,3.4.0
+      :doc:`rocThrust <rocthrust:index>`,4.2.0,4.1.0,3.3.0
      ,,,
      SUPPORT LIBS,,,
-      `hipother <https://github.com/ROCm/hipother>`_,7.0.51830,7.0.51830,6.4.43482
-      `rocm-core <https://github.com/ROCm/rocm-core>`_,7.0.2,7.0.1/7.0.0,6.4.0
+      `hipother <https://github.com/ROCm/hipother>`_,7.2.26015,7.1.52802,6.4.43482
+      `rocm-core <https://github.com/ROCm/rocm-core>`_,7.2.0,7.1.1,6.4.0
      `ROCT-Thunk-Interface <https://github.com/ROCm/ROCT-Thunk-Interface>`_,N/A [#ROCT-rocr]_,N/A [#ROCT-rocr]_,N/A [#ROCT-rocr]_
      ,,,
      SYSTEM MGMT TOOLS,.. _tools-support-compatibility-matrix:,,
-      :doc:`AMD SMI <amdsmi:index>`,26.0.2,26.0.0,25.3.0
-      :doc:`ROCm Data Center Tool <rdc:index>`,1.1.0,1.1.0,0.3.0
+      :doc:`AMD SMI <amdsmi:index>`,26.2.1,26.2.0,25.3.0
+      :doc:`ROCm Data Center Tool <rdc:index>`,1.2.0,1.2.0,0.3.0
      :doc:`rocminfo <rocminfo:index>`,1.0.0,1.0.0,1.0.0
      :doc:`ROCm SMI <rocm_smi_lib:index>`,7.8.0,7.8.0,7.5.0
-      :doc:`ROCm Validation Suite <rocmvalidationsuite:index>`,1.2.0,1.2.0,1.1.0
+      :doc:`ROCm Validation Suite <rocmvalidationsuite:index>`,1.3.0,1.3.0,1.1.0
      ,,,
      PERFORMANCE TOOLS,,,
      :doc:`ROCm Bandwidth Test <rocm_bandwidth_test:index>`,2.6.0,2.6.0,1.4.0
-      :doc:`ROCm Compute Profiler <rocprofiler-compute:index>`,3.2.3,3.2.3,3.1.0
-      :doc:`ROCm Systems Profiler <rocprofiler-systems:index>`,1.1.1,1.1.0,1.0.0
-      :doc:`ROCProfiler <rocprofiler:index>`,2.0.70002,2.0.70000,2.0.60400
-      :doc:`ROCprofiler-SDK <rocprofiler-sdk:index>`,1.0.0,1.0.0,0.6.0
-      :doc:`ROCTracer <roctracer:index>`,4.1.70002,4.1.70000,4.1.60400
+      :doc:`ROCm Compute Profiler <rocprofiler-compute:index>`,3.4.0,3.3.1,3.1.0
+      :doc:`ROCm Systems Profiler <rocprofiler-systems:index>`,1.3.0,1.2.1,1.0.0
+      :doc:`ROCProfiler <rocprofiler:index>`,2.0.70200,2.0.70101,2.0.60400
+      :doc:`ROCprofiler-SDK <rocprofiler-sdk:index>`,1.1.0,1.0.0,0.6.0
+      :doc:`ROCTracer <roctracer:index>`,4.1.70200,4.1.70101,4.1.60400
      ,,,
      DEVELOPMENT TOOLS,,,
-      :doc:`HIPIFY <hipify:index>`,20.0.0,20.0.0,19.0.0
+      :doc:`HIPIFY <hipify:index>`,22.0.0,20.0.0,19.0.0
      :doc:`ROCm CMake <rocmcmakebuildtools:index>`,0.14.0,0.14.0,0.14.0
-      :doc:`ROCdbgapi <rocdbgapi:index>`,0.77.4,0.77.3,0.77.2
+      :doc:`ROCdbgapi <rocdbgapi:index>`,0.77.4,0.77.4,0.77.2
      :doc:`ROCm Debugger (ROCgdb) <rocgdb:index>`,16.3.0,16.3.0,15.2.0
      `rocprofiler-register <https://github.com/ROCm/rocprofiler-register>`_,0.5.0,0.5.0,0.4.0
      :doc:`ROCr Debug Agent <rocr_debug_agent:index>`,2.1.0,2.1.0,2.0.4
@@ -143,86 +142,33 @@ compatibility and system requirements.
      COMPILERS,.. _compilers-support-compatibility-matrix:,,
      `clang-ocl <https://github.com/ROCm/clang-ocl>`_,N/A,N/A,N/A
      :doc:`hipCC <hipcc:index>`,1.1.1,1.1.1,1.1.1
-      `Flang <https://github.com/ROCm/flang>`_,20.0.0.25385,20.0.0.25314,19.0.0.25133
-      :doc:`llvm-project <llvm-project:index>`,20.0.0.25385,20.0.0.25314,19.0.0.25133
-      `OpenMP <https://github.com/ROCm/llvm-project/tree/amd-staging/openmp>`_,20.0.0.25385,20.0.0.25314,19.0.0.25133
+      `Flang <https://github.com/ROCm/flang>`_,22.0.0.26014,20.0.025444,19.0.0.25133
+      :doc:`llvm-project <llvm-project:index>`,22.0.0.26014,20.0.025444,19.0.0.25133
+      `OpenMP <https://github.com/ROCm/llvm-project/tree/amd-staging/openmp>`_,22.0.0.26014,20.0.025444,19.0.0.25133
      ,,,
      RUNTIMES,.. _runtime-support-compatibility-matrix:,,
-      :doc:`AMD CLR <hip:understand/amd_clr>`,7.0.51831,7.0.51830,6.4.43482
-      :doc:`HIP <hip:index>`,7.0.51831,7.0.51830,6.4.43482
+      :doc:`AMD CLR <hip:understand/amd_clr>`,7.2.26015,7.1.52802,6.4.43482
+      :doc:`HIP <hip:index>`,7.2.26015,7.1.52802,6.4.43482
      `OpenCL Runtime <https://github.com/ROCm/clr/tree/develop/opencl>`_,2.0.0,2.0.0,2.0.0
      :doc:`ROCr Runtime <rocr-runtime:index>`,1.18.0,1.18.0,1.15.0

+
 .. rubric:: Footnotes

-.. [#rhel-10-702] RHEL 10.0 and RHEL 9.6 are supported on all listed :ref:`supported_GPUs` except AMD Radeon PRO V620 GPU.
-.. [#rhel-94-702] RHEL 9.4 is supported on all AMD Instinct GPUs listed under :ref:`supported_GPUs`.
-.. [#rhel-700] RHEL 8.10 is supported only on AMD Instinct MI300X, MI300A, MI250X, MI250, MI210, and MI100 GPUs.
-.. [#ol-700-mi300x] **For ROCm 7.0.x** - Oracle Linux 10 and 9 are supported only on AMD Instinct MI355X, MI350X, and MI300X GPUs. Oracle Linux 8 is supported only on AMD Instinct MI300X GPU.
-.. [#ol-mi300x] **Prior ROCm 7.0.0** - Oracle Linux is supported only on AMD Instinct MI300X GPUs.
-.. [#db-mi300x] **For ROCm 7.0.2** - Debian 13 is supported only on AMD Instinct MI300X GPUs.
-.. [#sles-db-700] **For ROCm 7.0.x** - SLES 15 SP7 and Debian 12 are supported only on AMD Instinct MI300X, MI300A, MI250X, MI250, and MI210 GPUs.
-.. [#az-mi300x] Starting ROCm 6.4.0, Azure Linux 3.0 is supported only on AMD Instinct MI300X and AMD Radeon PRO V710 GPUs.
-.. [#rl-700] Rocky Linux 9 is supported only on AMD Instinct MI300X and MI300A GPUs.
-.. [#single-node] **Prior to ROCm 7.0.0** - Debian 12 is supported only on AMD Instinct MI300X GPUs for single-node functionality.
-.. [#mi350x-os] AMD Instinct MI355X (gfx950) and MI350X(gfx950) GPUs are supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 10.0, RHEL 9.6, RHEL 9.4, Oracle Linux 10, and Oracle Linux 9.
-.. [#RDNA-OS-700] **For ROCm 7.0.x** - AMD Radeon PRO AI PRO R9700 (gfx1201), AMD Radeon RX 9070 XT (gfx1201), AMD Radeon RX 9070 GRE (gfx1201), AMD Radeon RX 9070 (gfx1201), AMD Radeon RX 9060 XT (gfx1200), AMD Radeon RX 9060 (gfx1200), AMD Radeon RX 7800 XT (gfx1101), AMD Radeon RX 7700 XT (gfx1101), AMD Radeon PRO W7700 (gfx1101), and AMD Radeon PRO W6800 (gfx1030) are supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 10.0, and RHEL 9.6.
-.. [#rd-v710] **For ROCm 7.0.x** - AMD Radeon PRO V710 (gfx1101) GPUs are supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 10.0, RHEL 9.6, and Azure Linux 3.0.
-.. [#rd-v620] **For ROCm 7.0.x** - AMD Radeon PRO V620 (gfx1030) GPUs are supported only on Ubuntu 24.04.3 and Ubuntu 22.04.5.
-.. [#mi325x-os] **For ROCm 7.0.x** - AMD Instinct MI325X GPUs (gfx942) are supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 9.6, and RHEL 9.4.
-.. [#mi300x-os] **For ROCm 7.0.x** - AMD Instinct MI300X GPUs (gfx942) are supported on all listed :ref:`supported_distributions`.
-.. [#mi300A-os] **For ROCm 7.0.x** - AMD Instinct MI300A GPUs (gfx942) are supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 10.0, RHEL 9.6, RHEL 9.4, RHEL 8.10, SLES 15 SP7, Debian 12, and Rocky Linux 9.
-.. [#mi200x-os] **For ROCm 7.0.x** - AMD Instinct MI200 Series GPUs (gfx90a) are supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 10.0, RHEL 9.6, RHEL 9.4, RHEL 8.10, SLES 15 SP7, and Debian 12.
-.. [#mi100-os] **For ROCm 7.0.x** - AMD Instinct MI100 GPUs (gfx908) are supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 10.0, RHEL 9.6, RHEL 9.4, and RHEL 8.10.
-.. [#tf-mi350] TensorFlow 2.17.1 is not supported on AMD Instinct MI350 Series GPUs. Use TensorFlow 2.19.1 or 2.18.1 with MI350 Series GPUs instead.
-.. [#dgl_compat] DGL is supported only on ROCm 6.4.0.
-.. [#llama-cpp_compat] llama.cpp is supported only on ROCm 7.0.0 and ROCm 6.4.x.
+.. [#os-compatibility] Some operating systems are supported on specific GPUs. For detailed information about operating systems supported on ROCm 7.2.0, see the latest :ref:`supported_distributions`. For version specific information, see `ROCm 7.1.1 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-7.1.1/reference/system-requirements.html#supported-operating-systems>`__, and `ROCm 6.4.0 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.0/reference/system-requirements.html#supported-operating-systems>`__.
+.. [#gpu-compatibility] Some GPUs have limited operating system support. For detailed information about GPUs supporting ROCm 7.2.0, see the latest :ref:`supported_GPUs`. For version specific information, see `ROCm 7.1.1 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-7.1.1/reference/system-requirements.html#supported-gpus>`__, `ROCm 7.1.0 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-7.1.0/reference/system-requirements.html#supported-gpus>`__, and `ROCm 6.4.0 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.0/reference/system-requirements.html#supported-gpus>`__.
+.. [#dgl_compat] DGL is only supported on ROCm 7.0.0, 6.4.3 and 6.4.0.
+.. [#mi325x_KVM] For AMD Instinct MI325X KVM SR-IOV users, do not use AMD GPU Driver (amdgpu) 30.20.0.
 .. [#driver_patch] AMD GPU Driver (amdgpu) 30.10.1 is a quality release that resolves an issue identified in the 30.10 release. There are no other significant changes or feature additions in ROCm 7.0.1 from ROCm 7.0.0. AMD GPU Driver (amdgpu) 30.10.1 is compatible with ROCm 7.0.1 and ROCm 7.0.0.
 .. [#kfd_support] As of ROCm 6.4.0, forward and backward compatibility between the AMD GPU Driver (amdgpu) and its user space software is provided up to a year apart. For earlier ROCm releases, the compatibility is provided for +/- 2 releases. The supported user space versions on this page were accurate as of the time of initial ROCm release. For the most up-to-date information, see the latest version of this information at `User and AMD GPU Driver support matrix <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/user-kernel-space-compat-matrix.html>`_.
 .. [#ROCT-rocr] Starting from ROCm 6.3.0, the ROCT Thunk Interface is included as part of the ROCr runtime package.

-
 .. _OS-kernel-versions:

 Operating systems, kernel and Glibc versions
 *********************************************

-Use this lookup table to confirm which operating system and kernel versions are supported with ROCm.
-
-.. csv-table::
-   :header: "OS", "Version", "Kernel", "Glibc"
-   :widths: 40, 20, 30, 20
-   :stub-columns: 1
-
-   `Ubuntu <https://ubuntu.com/about/release-cycle#ubuntu-kernel-release-cycle>`_, 24.04.3, "6.8 [GA], 6.14 [HWE]", 2.39
-   ,,
-   `Ubuntu <https://ubuntu.com/about/release-cycle#ubuntu-kernel-release-cycle>`_, 24.04.2, "6.8 [GA], 6.11 [HWE]", 2.39
-   ,,
-   `Ubuntu <https://ubuntu.com/about/release-cycle#ubuntu-kernel-release-cycle>`_, 22.04.5, "5.15 [GA], 6.8 [HWE]", 2.35
-   ,,
-   `Red Hat Enterprise Linux (RHEL 10) <https://access.redhat.com/articles/3078#RHEL9>`_, 10.0, 6.12.0-55, 2.39
-   ,,
-   `Red Hat Enterprise Linux (RHEL 9) <https://access.redhat.com/articles/3078#RHEL9>`_, 9.6, 5.14.0-570, 2.34
-   ,9.5, 5.14+, 2.34
-   ,9.4, 5.14.0-427, 2.34
-   ,,
-   `Red Hat Enterprise Linux (RHEL 8) <https://access.redhat.com/articles/3078#RHEL8>`_, 8.10, 4.18.0-553, 2.28
-   ,,
-   `SUSE Linux Enterprise Server (SLES) <https://www.suse.com/support/kb/doc/?id=000019587#SLE15SP4>`_, 15 SP7, 6.40-150700.51, 2.38
-   ,15 SP6, "6.5.0+, 6.4.0", 2.38
-   ,15 SP5, 5.14.21, 2.31
-   ,,
-   `Rocky Linux <https://wiki.rockylinux.org/rocky/version/>`_, 9, 5.14.0-570, 2.34
-   ,,
-   `Oracle Linux <https://blogs.oracle.com/scoter/post/oracle-linux-and-unbreakable-enterprise-kernel-uek-releases>`_, 10, 6.12.0 (UEK), 2.39
-   ,9, 6.12.0 (UEK), 2.34
-   ,8, 5.15.0 (UEK), 2.28
-   ,,
-   `Debian <https://www.debian.org/download>`_,13, 6.12, 2.35
-   ,12, 6.1.0, 2.36
-   ,,
-   `Azure Linux <https://techcommunity.microsoft.com/blog/linuxandopensourceblog/azure-linux-3-0-now-in-preview-on-azure-kubernetes-service-v1-31/4287229>`_,3.0, 6.6.92, 2.38
-   ,,
+For detailed information on operating system supported on ROCm 7.2.0 and associated Kernel and Glibc version, see the latest :ref:`supported_distributions`. For version specific information, see `ROCm 7.1.1 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-7.1.1/reference/system-requirements.html#supported-operating-systems>`__, and `ROCm 6.4.0 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.0/reference/system-requirements.html#supported-operating-systems>`__.

 .. note::

@@ -254,46 +200,11 @@ Expand for full historical view of:

   .. rubric:: Footnotes

-   .. [#rhel-10-702-past-60] RHEL 10.0 and RHEL 9.6 are supported on all listed :ref:`supported_GPUs` except AMD Radeon PRO V620 GPU.
-   .. [#rhel-94-702-past-60] RHEL 9.4 is supported on all AMD Instinct GPUs listed under :ref:`supported_GPUs`.
-   .. [#rhel-700-past-60] **For ROCm 7.0.x** - RHEL 8.10 is supported only on AMD Instinct MI300X, MI300A, MI250X, MI250, MI210, and MI100 GPUs.
-   .. [#ol-700-mi300x-past-60] **For ROCm 7.0.x** - Oracle Linux 10 and 9 are supported only on AMD Instinct MI355X, MI350X, and MI300X GPUs. Oracle Linux 8 is supported only on AMD Instinct MI300X GPU.
-   .. [#mi300x-past-60] **Prior ROCm 7.0.0** - Oracle Linux is supported only on AMD Instinct MI300X GPUs.
-   .. [#db-mi300x-past-60] **For ROCm 7.0.2** - Debian 13 is supported only on AMD Instinct MI300X GPUs.
-   .. [#sles-db-700-past-60] **For ROCm 7.0.x** - SLES 15 SP7 and Debian 12 are supported only on AMD Instinct MI300X, MI300A, MI250X, MI250, and MI210 GPUs.
-   .. [#single-node-past-60] **Prior to ROCm 7.0.0** - Debian 12 is supported only on AMD Instinct MI300X GPUs for single-node functionality.
-   .. [#az-mi300x-past-60] Starting from ROCm 6.4.0, Azure Linux 3.0 is supported only on AMD Instinct MI300X and AMD Radeon PRO V710 GPUs.
-   .. [#az-mi300x-630-past-60] **Prior ROCm 6.4.0**- Azure Linux 3.0 is supported only on AMD Instinct MI300X GPUs.
-   .. [#rl-700-past-60] Rocky Linux 9 is supported only on AMD Instinct MI300X and MI300A GPUs.
-   .. [#mi350x-os-past-60] AMD Instinct MI355X (gfx950) and MI350X(gfx950) GPUs are supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 9.6, RHEL 9.4, and Oracle Linux 9.
-   .. [#RDNA-OS-700-past-60] **For ROCm 7.0.x** AMD Radeon PRO AI PRO R9700 (gfx1201), AMD Radeon RX 9070 XT (gfx1201), AMD Radeon RX 9070 GRE (gfx1201), AMD Radeon RX 9070 (gfx1201), AMD Radeon RX 9060 XT (gfx1200), AMD Radeon RX 9060 (gfx1200), AMD Radeon RX 7800 XT (gfx1101), AMD Radeon RX 7700 XT (gfx1101), AMD Radeon PRO W7700 (gfx1101), and AMD Radeon PRO W6800 (gfx1030) are supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 10.0, RHEL 9.6, RHEL 9.4, Oracle Linux 10, and Oracle Linux 9.
-   .. [#RDNA-OS-past-60] **Prior ROCm 7.0.0** - Radeon AI PRO R9700, Radeon RX 9070 XT (gfx1201), Radeon RX 9060 XT (gfx1200), Radeon PRO W7700 (gfx1101), and Radeon RX 7800 XT (gfx1101) are supported only on Ubuntu 24.04.2, Ubuntu 22.04.5, RHEL 9.6, and RHEL 9.4.
-   .. [#rd-v710-past-60] **For ROCm 7.0.x** - AMD Radeon PRO V710 (gfx1101) is supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 10.0, RHEL 9.6, and Azure Linux 3.0.
-   .. [#rd-v620-past-60] **For ROCm 7.0.x** - AMD Radeon PRO V620 (gfx1030) is supported only on Ubuntu 24.04.3 and Ubuntu 22.04.5.
-   .. [#mi325x-os-past-60] **For ROCm 7.0.x** - AMD Instinct MI325X GPU (gfx942) is supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 9.6, and RHEL 9.4.
-   .. [#mi300x-os-past-60] **For ROCm 7.0.x** - AMD Instinct MI300X GPU (gfx942) is supported on all listed :ref:`supported_distributions`.
-   .. [#mi300A-os-past-60] **For ROCm 7.0.x** - AMD Instinct MI300A GPU (gfx942) is supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 10.0, RHEL 9.6, RHEL 9.4, RHEL 8.10, SLES 15 SP7, Debian 12, and Rocky Linux 9.
-   .. [#mi200x-os-past-60] **For ROCm 7.0.x** - AMD Instinct MI200 Series GPUs (gfx90a) are supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 10.0, RHEL 9.6, RHEL 9.4, RHEL 8.10, SLES 15 SP7, and Debian 12.
-   .. [#mi100-os-past-60] **For ROCm 7.0.x** - AMD Instinct MI100 GPU (gfx908) is supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 10.0, RHEL 9.6, RHEL 9.4, and RHEL 8.10.
-   .. [#7700XT-OS-past-60] **Prior to ROCm 7.0.0** - Radeon RX 7700 XT (gfx1101) is supported only on Ubuntu 24.04.2 and RHEL 9.6.
-   .. [#mi300_624-past-60] **For ROCm 6.2.4** - MI300X (gfx942) is supported on listed operating systems *except* Ubuntu 22.04.5 [6.8 HWE] and Ubuntu 22.04.4 [6.5 HWE].
-   .. [#mi300_622-past-60] **For ROCm 6.2.2** - MI300X (gfx942) is supported on listed operating systems *except* Ubuntu 22.04.5 [6.8 HWE] and Ubuntu 22.04.4 [6.5 HWE].
-   .. [#mi300_621-past-60] **For ROCm 6.2.1** - MI300X (gfx942) is supported on listed operating systems *except* Ubuntu 22.04.5 [6.8 HWE] and Ubuntu 22.04.4 [6.5 HWE].
-   .. [#mi300_620-past-60] **For ROCm 6.2.0** - MI300X (gfx942) is supported on listed operating systems *except* Ubuntu 22.04.5 [6.8 HWE] and Ubuntu 22.04.4 [6.5 HWE].
-   .. [#mi300_612-past-60] **For ROCm 6.1.2** - MI300A (gfx942) is supported on Ubuntu 22.04.4, RHEL 9.4, RHEL 9.3, RHEL 8.9, and SLES 15 SP5. MI300X (gfx942) is supported only on Ubuntu 22.04.4 and Oracle Linux.
-   .. [#mi300_611-past-60] **For ROCm 6.1.1** - MI300A (gfx942) is supported on Ubuntu 22.04.4, RHEL 9.4, RHEL 9.3, RHEL 8.9, and SLES 15 SP5. MI300X (gfx942) is supported only on Ubuntu 22.04.4 and Oracle Linux.
-   .. [#mi300_610-past-60] **For ROCm 6.1.0** - MI300A (gfx942) is supported on Ubuntu 22.04.4, RHEL 9.4, RHEL 9.3, RHEL 8.9, and SLES 15 SP5. MI300X (gfx942) is supported only on Ubuntu 22.04.4.
-   .. [#mi300_602-past-60] **For ROCm 6.0.2** - MI300A (gfx942) is supported on Ubuntu 22.04.3, RHEL 8.9, and SLES 15 SP5. MI300X (gfx942) is supported only on Ubuntu 22.04.3.
-   .. [#mi300_600-past-60] **For ROCm 6.0.0** - MI300A (gfx942) is supported on Ubuntu 22.04.3, RHEL 8.9, and SLES 15 SP5. MI300X (gfx942) is supported only on Ubuntu 22.04.3.
+   .. [#os-compatibility-past-60] Some operating systems are supported on specific GPUs. For detailed information, see :ref:`supported_distributions` and select the required ROCm version for version specific support.
+   .. [#gpu-compatibility-past-60] Some GPUs have limited operating system support. For detailed information, see :ref:`supported_GPUs` and select the required ROCm version for version specific support.
   .. [#tf-mi350-past-60] TensorFlow 2.17.1 is not supported on AMD Instinct MI350 Series GPUs. Use TensorFlow 2.19.1 or 2.18.1 with MI350 Series GPUs instead.
-   .. [#verl_compat-past-60] verl is supported only on ROCm 6.2.0.
-   .. [#stanford-megatron-lm_compat-past-60] Stanford Megatron-LM is supported only on ROCm 6.3.0.
-   .. [#dgl_compat-past-60] DGL is supported only on ROCm 6.4.0.
-   .. [#megablocks_compat-past-60] Megablocks is supported only on ROCm 6.3.0.
-   .. [#taichi_compat-past-60] Taichi is supported only on ROCm 6.3.2.
-   .. [#ray_compat-past-60] Ray is supported only on ROCm 6.4.1.
-   .. [#llama-cpp_compat-past-60] llama.cpp is supported only on ROCm 7.0.0 and 6.4.x.
-   .. [#flashinfer_compat-past-60] FlashInfer is supported only on ROCm 6.4.1.
+   .. [#dgl_compat-past-60] DGL is only supported on ROCm 7.0.0, 6.4.3 and 6.4.0.
+   .. [#mi325x_KVM-past-60] For AMD Instinct MI325X KVM SR-IOV users, do not use AMD GPU Driver (amdgpu) 30.20.0.
   .. [#driver_patch-past-60] AMD GPU Driver (amdgpu) 30.10.1 is a quality release that resolves an issue identified in the 30.10 release. There are no other significant changes or feature additions in ROCm 7.0.1 from ROCm 7.0.0. AMD GPU Driver (amdgpu) 30.10.1 is compatible with ROCm 7.0.1 and ROCm 7.0.0.
   .. [#kfd_support-past-60] As of ROCm 6.4.0, forward and backward compatibility between the AMD GPU Driver (amdgpu) and its user space software is provided up to a year apart. For earlier ROCm releases, the compatibility is provided for +/- 2 releases. The supported user space versions on this page were accurate as of the time of initial ROCm release. For the most up-to-date information, see the latest version of this information at `User and AMD GPU Driver support matrix <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/user-kernel-space-compat-matrix.html>`_.
   .. [#ROCT-rocr-past-60] Starting from ROCm 6.3.0, the ROCT Thunk Interface is included as part of the ROCr runtime package.
@@ -2,7 +2,7 @@

 .. meta::
    :description: Deep Graph Library (DGL) compatibility
-    :keywords: GPU, DGL compatibility
+    :keywords: GPU, CPU, deep graph library, DGL, deep learning, framework compatibility

 .. version-set:: rocm_version latest

@@ -10,215 +10,274 @@
 DGL compatibility
 ********************************************************************************

-Deep Graph Library `(DGL) <https://www.dgl.ai/>`_ is an easy-to-use, high-performance and scalable 
+Deep Graph Library (`DGL <https://www.dgl.ai/>`__) is an easy-to-use, high-performance, and scalable 
 Python package for deep learning on graphs. DGL is framework agnostic, meaning 
-if a deep graph model is a component in an end-to-end application, the rest of 
+that if a deep graph model is a component in an end-to-end application, the rest of 
 the logic is implemented using PyTorch.  

-* ROCm support for DGL is hosted in the `https://github.com/ROCm/dgl <https://github.com/ROCm/dgl>`_ repository. 
-* Due to independent compatibility considerations, this location differs from the `https://github.com/dmlc/dgl <https://github.com/dmlc/dgl>`_ upstream repository. 
-* Use the prebuilt :ref:`Docker images <dgl-docker-compat>` with DGL, PyTorch, and ROCm preinstalled.
-* See the :doc:`ROCm DGL installation guide <rocm-install-on-linux:install/3rd-party/dgl-install>` 
-  to install and get started.
+DGL provides a high-performance graph object that can reside on either CPUs or GPUs. 
+It bundles structural data features for better control and provides a variety of functions 
+for computing with graph objects, including efficient and customizable message passing 
+primitives for Graph Neural Networks.

-
-Supported devices
+Support overview
 ================================================================================

- **Officially Supported**: TF32 with AMD Instinct MI300X (through hipblaslt)
- **Partially Supported**: TF32 with AMD Instinct MI250X
+- The ROCm-supported version of DGL is maintained in the official `https://github.com/ROCm/dgl 
+  <https://github.com/ROCm/dgl>`__ repository, which differs from the 
+  `https://github.com/dmlc/dgl <https://github.com/dmlc/dgl>`__ upstream repository.

+- To get started and install DGL on ROCm, use the prebuilt :ref:`Docker images <dgl-docker-compat>`, 
+  which include ROCm, DGL, and all required dependencies.

-.. _dgl-recommendations:
-
-Use cases and recommendations
-================================================================================
-
-DGL can be used for Graph Learning, and building popular graph models like  
-GAT, GCN and GraphSage. Using these we can support a variety of use-cases such as:
-
- Recommender systems
- Network Optimization and Analysis
- 1D (Temporal) and 2D (Image) Classification
- Drug Discovery
-
-Multiple use cases of DGL have been tested and verified.
-However, a recommended example follows a drug discovery pipeline using the ``SE3Transformer``.
-Refer to the `AMD ROCm blog <https://rocm.blogs.amd.com/>`_, 
-where you can search for DGL examples and best practices to optimize your training workflows on AMD GPUs. 
-
-Coverage includes:
-
- Single-GPU training/inference
- Multi-GPU training
+  - See the :doc:`ROCm DGL installation guide <rocm-install-on-linux:install/3rd-party/dgl-install>` 
+    for installation and setup instructions.

+  - You can also consult the upstream `Installation guide <https://www.dgl.ai/pages/start.html>`__ 
+    for additional context.

 .. _dgl-docker-compat:

-Docker image compatibility
+Compatibility matrix
 ================================================================================

 .. |docker-icon| raw:: html

   <i class="fab fa-docker"></i>

-AMD validates and publishes `DGL images <https://hub.docker.com/r/rocm/dgl>`_
-with ROCm and Pytorch backends on Docker Hub. The following Docker image tags and associated
-inventories were tested on `ROCm 6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`_.
+AMD validates and publishes `DGL images <https://hub.docker.com/r/rocm/dgl/tags>`__
+with ROCm backends on Docker Hub. The following Docker image tags and associated
+inventories represent the latest available DGL version from the official Docker Hub. 
 Click the |docker-icon| to view the image on Docker Hub.

-.. list-table:: DGL Docker image components
+.. list-table::
    :header-rows: 1
    :class: docker-image-compatibility

-    * - Docker
+    * - Docker image
+      - ROCm
      - DGL
      - PyTorch
      - Ubuntu
      - Python
+      - GPU

    * - .. raw:: html

-           <a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4_rocm6.4_ubuntu24.04_py3.12_pytorch_release_2.6.0/images/sha256-8ce2c3bcfaa137ab94a75f9e2ea711894748980f57417739138402a542dd5564"><i class="fab fa-docker fa-lg"></i></a>
+           <a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4.0.amd0_rocm7.0.0_ubuntu24.04_py3.12_pytorch_2.8.0/images/sha256-943698ddf54c22a7bcad2e5b4ff467752e29e4ba6d0c926789ae7b242cbd92dd"><i class="fab fa-docker fa-lg"></i> rocm/dgl</a>

-      - `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`_
-      - `2.6.0 <https://github.com/ROCm/pytorch/tree/release/2.6>`_
+      - `7.0.0 <https://repo.radeon.com/rocm/apt/7.0/>`__
+      - `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`__
+      - `2.8.0 <https://github.com/pytorch/pytorch/releases/tag/v2.8.0>`__
      - 24.04
-      - `3.12.9 <https://www.python.org/downloads/release/python-3129/>`_
+      - `3.12.9 <https://www.python.org/downloads/release/python-3129/>`__
+      - MI300X, MI250X

    * - .. raw:: html

-           <a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4_rocm6.4_ubuntu24.04_py3.12_pytorch_release_2.4.1/images/sha256-cf1683283b8eeda867b690229c8091c5bbf1edb9f52e8fb3da437c49a612ebe4"><i class="fab fa-docker fa-lg"></i></a>
+           <a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4.0.amd0_rocm7.0.0_ubuntu24.04_py3.12_pytorch_2.6.0/images/sha256-b2ec286a035eb7d0a6aab069561914d21a3cac462281e9c024501ba5ccedfbf7"><i class="fab fa-docker fa-lg"></i> rocm/dgl</a>

-      - `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`_
-      - `2.4.1 <https://github.com/ROCm/pytorch/tree/release/2.4>`_
+      - `7.0.0 <https://repo.radeon.com/rocm/apt/7.0/>`__
+      - `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`__
+      - `2.6.0 <https://github.com/pytorch/pytorch/releases/tag/v2.6.0>`__
      - 24.04
-      - `3.12.9 <https://www.python.org/downloads/release/python-3129/>`_
-
+      - `3.12.9 <https://www.python.org/downloads/release/python-3129/>`__
+      - MI300X, MI250X

    * - .. raw:: html

-           <a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4_rocm6.4_ubuntu22.04_py3.10_pytorch_release_2.4.1/images/sha256-4834f178c3614e2d09e89e32041db8984c456d45dfd20286e377ca8635686554"><i class="fab fa-docker fa-lg"></i></a>
+           <a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4.0.amd0_rocm7.0.0_ubuntu22.04_py3.10_pytorch_2.7.1/images/sha256-d27aee16df922ccf0bcd9107bfcb6d20d34235445d456c637e33ca6f19d11a51"><i class="fab fa-docker fa-lg"></i> rocm/dgl</a>

-      - `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`_
-      - `2.4.1 <https://github.com/ROCm/pytorch/tree/release/2.4>`_
+      - `7.0.0 <https://repo.radeon.com/rocm/apt/7.0/>`__
+      - `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`__
+      - `2.7.1 <https://github.com/pytorch/pytorch/releases/tag/v2.7.1>`__
      - 22.04
-      - `3.10.16 <https://www.python.org/downloads/release/python-31016/>`_
-
+      - `3.10.16 <https://www.python.org/downloads/release/python-31016/>`__
+      - MI300X, MI250X

    * - .. raw:: html

-           <a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4_rocm6.4_ubuntu22.04_py3.10_pytorch_release_2.3.0/images/sha256-88740a2c8ab4084b42b10c3c6ba984cab33dd3a044f479c6d7618e2b2cb05e69"><i class="fab fa-docker fa-lg"></i></a>
+           <a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4.0.amd0_rocm6.4.3_ubuntu24.04_py3.12_pytorch_2.6.0/images/sha256-f3ba6a3c9ec9f6c1cde28449dc9780e0c4c16c4140f4b23f158565fbfd422d6b"><i class="fab fa-docker fa-lg"></i> rocm/dgl</a>

-      - `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`_
-      - `2.3.0 <https://github.com/ROCm/pytorch/tree/release/2.3>`_
+      - `6.4.3 <https://repo.radeon.com/rocm/apt/6.4.3/>`__
+      - `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`__
+      - `2.6.0 <https://github.com/pytorch/pytorch/releases/tag/v2.6.0>`__
+      - 24.04
+      - `3.12.9 <https://www.python.org/downloads/release/python-3129/>`__
+      - MI300X, MI250X
+
+    * - .. raw:: html
+
+           <a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4_rocm6.4_ubuntu24.04_py3.12_pytorch_release_2.6.0/images/sha256-8ce2c3bcfaa137ab94a75f9e2ea711894748980f57417739138402a542dd5564"><i class="fab fa-docker fa-lg"></i> rocm/dgl</a>
+
+      - `6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`__
+      - `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`__
+      - `2.6.0 <https://github.com/pytorch/pytorch/releases/tag/v2.6.0>`__
+      - 24.04
+      - `3.12.9 <https://www.python.org/downloads/release/python-3129/>`__
+      - MI300X, MI250X
+
+    * - .. raw:: html
+
+           <a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4_rocm6.4_ubuntu24.04_py3.12_pytorch_release_2.4.1/images/sha256-cf1683283b8eeda867b690229c8091c5bbf1edb9f52e8fb3da437c49a612ebe4"><i class="fab fa-docker fa-lg"></i> rocm/dgl</a>
+
+      - `6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`__
+      - `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`__
+      - `2.4.1 <https://github.com/pytorch/pytorch/releases/tag/v2.4.1>`__
+      - 24.04
+      - `3.12.9 <https://www.python.org/downloads/release/python-3129/>`__
+      - MI300X, MI250X
+
+    * - .. raw:: html
+
+           <a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4_rocm6.4_ubuntu22.04_py3.10_pytorch_release_2.4.1/images/sha256-4834f178c3614e2d09e89e32041db8984c456d45dfd20286e377ca8635686554"><i class="fab fa-docker fa-lg"></i> rocm/dgl</a>
+
+      - `6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`__
+      - `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`__
+      - `2.4.1 <https://github.com/pytorch/pytorch/releases/tag/v2.4.1>`__
      - 22.04
-      - `3.10.16 <https://www.python.org/downloads/release/python-31016/>`_
-      
+      - `3.10.16 <https://www.python.org/downloads/release/python-31016/>`__
+      - MI300X, MI250X
+
+    * - .. raw:: html
+
+           <a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4_rocm6.4_ubuntu22.04_py3.10_pytorch_release_2.3.0/images/sha256-88740a2c8ab4084b42b10c3c6ba984cab33dd3a044f479c6d7618e2b2cb05e69"><i class="fab fa-docker fa-lg"></i> rocm/dgl</a>
+
+      - `6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`__
+      - `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`__
+      - `2.3.0 <https://github.com/pytorch/pytorch/releases/tag/v2.3.0>`__
+      - 22.04
+      - `3.10.16 <https://www.python.org/downloads/release/python-31016/>`__
+      - MI300X, MI250X
+
+
+.. _dgl-key-rocm-libraries:

 Key ROCm libraries for DGL
 ================================================================================

 DGL on ROCm depends on specific libraries that affect its features and performance.
-Using the DGL Docker container or building it with the provided docker file or a ROCm base image is recommended.
+Using the DGL Docker container or building it with the provided Docker file or a ROCm base image is recommended.
 If you prefer to build it yourself, ensure the following dependencies are installed:

 .. list-table:: 
    :header-rows: 1

    * - ROCm library
-      - Version
+      - ROCm 7.0.0 Version
+      - ROCm 6.4.x Version
      - Purpose
    * - `Composable Kernel <https://github.com/ROCm/composable_kernel>`_
-      - :version-ref:`"Composable Kernel" rocm_version`
+      - 1.1.0
+      - 1.1.0
      - Enables faster execution of core operations like matrix multiplication
        (GEMM), convolutions and transformations.
    * - `hipBLAS <https://github.com/ROCm/hipBLAS>`_
-      - :version-ref:`hipBLAS rocm_version`
+      - 3.0.0
+      - 2.4.0
      - Provides GPU-accelerated Basic Linear Algebra Subprograms (BLAS) for
        matrix and vector operations.
    * - `hipBLASLt <https://github.com/ROCm/hipBLASLt>`_
-      - :version-ref:`hipBLASLt rocm_version`
+      - 1.0.0
+      - 0.12.0
      - hipBLASLt is an extension of the hipBLAS library, providing additional
        features like epilogues fused into the matrix multiplication kernel or
        use of integer tensor cores.
    * - `hipCUB <https://github.com/ROCm/hipCUB>`_
-      - :version-ref:`hipCUB rocm_version`
+      - 4.0.0
+      - 3.4.0
      - Provides a C++ template library for parallel algorithms for reduction,
        scan, sort and select.
    * - `hipFFT <https://github.com/ROCm/hipFFT>`_
-      - :version-ref:`hipFFT rocm_version`
+      - 1.0.20
+      - 1.0.18
      - Provides GPU-accelerated Fast Fourier Transform (FFT) operations.
    * - `hipRAND <https://github.com/ROCm/hipRAND>`_
-      - :version-ref:`hipRAND rocm_version`
+      - 3.0.0
+      - 2.12.0
      - Provides fast random number generation for GPUs.
    * - `hipSOLVER <https://github.com/ROCm/hipSOLVER>`_
-      - :version-ref:`hipSOLVER rocm_version`
+      - 3.0.0
+      - 2.4.0
      - Provides GPU-accelerated solvers for linear systems, eigenvalues, and
        singular value decompositions (SVD).
    * - `hipSPARSE <https://github.com/ROCm/hipSPARSE>`_
-      - :version-ref:`hipSPARSE rocm_version`
+      - 4.0.1
+      - 3.2.0
      - Accelerates operations on sparse matrices, such as sparse matrix-vector
        or matrix-matrix products.
    * - `hipSPARSELt <https://github.com/ROCm/hipSPARSELt>`_
-      - :version-ref:`hipSPARSELt rocm_version`
+      - 0.2.4
+      - 0.2.3
      - Accelerates operations on sparse matrices, such as sparse matrix-vector
        or matrix-matrix products.
    * - `hipTensor <https://github.com/ROCm/hipTensor>`_
-      - :version-ref:`hipTensor rocm_version`
+      - 2.0.0
+      - 1.5.0
      - Optimizes for high-performance tensor operations, such as contractions.
    * - `MIOpen <https://github.com/ROCm/MIOpen>`_
-      - :version-ref:`MIOpen rocm_version`
+      - 3.5.0
+      - 3.4.0
      - Optimizes deep learning primitives such as convolutions, pooling,
        normalization, and activation functions.
    * - `MIGraphX <https://github.com/ROCm/AMDMIGraphX>`_
-      - :version-ref:`MIGraphX rocm_version`
+      - 2.13.0
+      - 2.12.0
      - Adds graph-level optimizations, ONNX models and mixed precision support
        and enable Ahead-of-Time (AOT) Compilation.
    * - `MIVisionX <https://github.com/ROCm/MIVisionX>`_
-      - :version-ref:`MIVisionX rocm_version`
+      - 3.3.0
+      - 3.2.0
      - Optimizes acceleration for computer vision and AI workloads like
        preprocessing, augmentation, and inferencing.
    * - `rocAL <https://github.com/ROCm/rocAL>`_
-      - :version-ref:`rocAL rocm_version`
+      - 3.3.0
+      - 2.2.0
      - Accelerates the data pipeline by offloading intensive preprocessing and
        augmentation tasks. rocAL is part of MIVisionX.
    * - `RCCL <https://github.com/ROCm/rccl>`_
-      - :version-ref:`RCCL rocm_version`
+      - 2.26.6
+      - 2.22.3
      - Optimizes for multi-GPU communication for operations like AllReduce and
        Broadcast.
    * - `rocDecode <https://github.com/ROCm/rocDecode>`_
-      - :version-ref:`rocDecode rocm_version`
+      - 1.0.0
+      - 0.10.0
      - Provides hardware-accelerated data decoding capabilities, particularly
        for image, video, and other dataset formats.
    * - `rocJPEG <https://github.com/ROCm/rocJPEG>`_
-      - :version-ref:`rocJPEG rocm_version`
+      - 1.1.0
+      - 0.8.0
      - Provides hardware-accelerated JPEG image decoding and encoding.
    * - `RPP <https://github.com/ROCm/RPP>`_
-      - :version-ref:`RPP rocm_version`
+      - 2.0.0
+      - 1.9.10
      - Speeds up data augmentation, transformation, and other preprocessing steps.
    * - `rocThrust <https://github.com/ROCm/rocThrust>`_
-      - :version-ref:`rocThrust rocm_version`
+      - 4.0.0
+      - 3.3.0
      - Provides a C++ template library for parallel algorithms like sorting,
        reduction, and scanning.
    * - `rocWMMA <https://github.com/ROCm/rocWMMA>`_
-      - :version-ref:`rocWMMA rocm_version`
+      - 2.0.0
+      - 1.7.0
      - Accelerates warp-level matrix-multiply and matrix-accumulate to speed up matrix
        multiplication (GEMM) and accumulation operations with mixed precision
        support.

+.. _dgl-supported-features-latest:

-Supported features
+Supported features with ROCm 7.0.0
 ================================================================================

-Many functions and methods available in DGL Upstream are also supported in DGL ROCm.
+Many functions and methods available upstream are also supported in DGL on ROCm.
 Instead of listing them all, support is grouped into the following categories to provide a general overview. 

 * DGL Base
 * DGL Backend 
 * DGL Data
 * DGL Dataloading
-* DGL DGLGraph
+* DGL Graph
 * DGL Function
 * DGL Ops
 * DGL Sampling
@@ -230,26 +289,76 @@ Instead of listing them all, support is grouped into the following categories to
 * DGL NN
 * DGL Optim
 * DGL Sparse
+* GraphBolt

+.. _dgl-unsupported-features-latest:

-Unsupported features
+Unsupported features with ROCm 7.0.0
 ================================================================================

-* Graphbolt
-* Partial TF32 Support (MI250x only)
-* Kineto/ ROCTracer integration
+* TF32 Support (only supported for PyTorch 2.7 and above)
+* Kineto/ROCTracer integration

+.. _dgl-unsupported-functions:

-Unsupported functions
+Unsupported functions with ROCm 7.0.0
 ================================================================================

-* ``more_nnz``
+* ``bfs``
 * ``format``
 * ``multiprocess_sparse_adam_state_dict``
-* ``record_stream_ndarray``
 * ``half_spmm``
 * ``segment_mm`` 
 * ``gather_mm_idx_b``
-* ``pgexplainer``
 * ``sample_labors_prob``
 * ``sample_labors_noprob``
+* ``sparse_admin``
+
+.. _dgl-recommendations:
+
+Use cases and recommendations
+================================================================================
+
+DGL can be used for Graph Learning, and building popular graph models like  
+GAT, GCN, and GraphSage. Using these models, a variety of use cases are supported:
+
+- Recommender systems
+- Network Optimization and Analysis
+- 1D (Temporal) and 2D (Image) Classification
+- Drug Discovery
+
+For use cases and recommendations, refer to the `AMD ROCm blog <https://rocm.blogs.amd.com/>`__, 
+where you can search for DGL examples and best practices to optimize your workloads on AMD GPUs.
+
+* Although multiple use cases of DGL have been tested and verified, a few have been  
+  outlined in the `DGL in the Real World: Running GNNs on Real Use Cases 
+  <https://rocm.blogs.amd.com/artificial-intelligence/dgl_blog2/README.html>`__ blog 
+  post, which walks through four real-world graph neural network (GNN) workloads 
+  implemented with the Deep Graph Library on ROCm. It covers tasks ranging from 
+  heterogeneous e-commerce graphs and multiplex networks (GATNE) to molecular graph 
+  regression (GNN-FiLM) and EEG-based neurological diagnosis (EEG-GCNN). For each use 
+  case, the authors detail: the dataset and task, how DGL is used, and their experience 
+  porting to ROCm. It is shown that DGL codebases often run without modification, with 
+  seamless integration of graph operations, message passing, sampling, and convolution. 
+
+* The `Graph Neural Networks (GNNs) at Scale: DGL with ROCm on AMD Hardware 
+  <https://rocm.blogs.amd.com/artificial-intelligence/why-graph-neural/README.html>`__ 
+  blog post introduces the Deep Graph Library (DGL) and its enablement on the AMD ROCm platform, 
+  bringing high-performance graph neural network (GNN) training to AMD GPUs. DGL bridges 
+  the gap between dense tensor frameworks and the irregular nature of graph data through a 
+  graph-first, message-passing abstraction. Its design ensures scalability, flexibility, and 
+  interoperability across frameworks like PyTorch and TensorFlow. AMD’s ROCm integration 
+  enables DGL to run efficiently on HIP-based GPUs, supported by prebuilt Docker containers 
+  and open-source repositories. This marks a major step in AMD's mission to advance open, 
+  scalable AI ecosystems beyond traditional architectures.
+
+You can pre-process datasets and begin training on AMD GPUs through:
+
+* Single-GPU training/inference
+* Multi-GPU training
+
+
+Previous versions
+===============================================================================
+See :doc:`rocm-install-on-linux:install/3rd-party/previous-versions/dgl-history` to find documentation for previous releases
+of the ``ROCm/dgl`` Docker image.
@@ -1,107 +0,0 @@
-:orphan:
-
-.. meta::
-    :description: FlashInfer deep learning framework compatibility
-    :keywords: GPU, LLM, FlashInfer, compatibility
-
-.. version-set:: rocm_version latest
-
-********************************************************************************
-FlashInfer compatibility
-********************************************************************************
-
-`FlashInfer <https://docs.flashinfer.ai/index.html>`__ is a library and kernel generator 
-for Large Language Models (LLMs) that provides high-performance implementation of graphics 
-processing units (GPUs) kernels. FlashInfer focuses on LLM serving and inference, as well 
-as advanced performance across diverse scenarios.
-
-FlashInfer features highly efficient attention kernels, load-balanced scheduling, and memory-optimized 
-techniques, while supporting customized attention variants. It’s compatible with ``torch.compile``, and 
-offers high-performance LLM-specific operators, with easy integration through PyTorch, and C++ APIs.
-
-.. note::
-
-  The ROCm port of FlashInfer is under active development, and some features are not yet available. 
-  For the latest feature compatibility matrix, refer to the ``README`` of the 
-  `https://github.com/ROCm/flashinfer <https://github.com/ROCm/flashinfer>`__ repository.
-
-Support for the ROCm port of FlashInfer is available as follows:
-
- ROCm support for FlashInfer is hosted in the `https://github.com/ROCm/flashinfer 
-  <https://github.com/ROCm/flashinfer>`__ repository. This location differs from the 
-  `https://github.com/flashinfer-ai/flashinfer <https://github.com/flashinfer-ai/flashinfer>`_ 
-  upstream repository.
-
- To install FlashInfer, use the prebuilt :ref:`Docker image <flashinfer-docker-compat>`, 
-  which includes ROCm, FlashInfer, and all required dependencies.
-
-  - See the :doc:`ROCm FlashInfer installation guide <rocm-install-on-linux:install/3rd-party/flashinfer-install>` 
-    to install and get started.
-
-  - See the `Installation guide <https://docs.flashinfer.ai/installation.html>`__ 
-    in the upstream FlashInfer documentation.
-
-.. note::
-
-  Flashinfer is supported on ROCm 6.4.1.
-
-Supported devices
-================================================================================
-
-**Officially Supported**: AMD Instinct™ MI300X
-
-
-.. _flashinfer-recommendations:
-
-Use cases and recommendations
-================================================================================
-
-This release of FlashInfer on ROCm provides the decode functionality for LLM inferencing.
-In the decode phase, tokens are generated sequentially, with the model predicting each new 
-token based on the previously generated tokens and the input context.
-
-FlashInfer on ROCm brings over upstream features such as load balancing, sparse and dense 
-attention optimizations, and batching support, enabling efficient execution on AMD Instinct™ MI300X GPUs.
-
-Because large LLMs often require substantial KV caches or long context windows, FlashInfer on ROCm 
-also implements cascade attention from upstream to reduce memory usage. 
-
-For currently supported use cases and recommendations, refer to the `AMD ROCm blog <https://rocm.blogs.amd.com/>`__, 
-where you can search for examples and best practices to optimize your workloads on AMD GPUs.
-
-.. _flashinfer-docker-compat:
-
-Docker image compatibility
-================================================================================
-
-.. |docker-icon| raw:: html
-
-   <i class="fab fa-docker"></i>
-
-AMD validates and publishes `ROCm FlashInfer images <https://hub.docker.com/r/rocm/flashinfer/tags>`__
-with ROCm and Pytorch backends on Docker Hub. The following Docker image tags and associated
-inventories represent the FlashInfer version from the official Docker Hub.
-The Docker images have been validated for `ROCm 6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`__.
-Click |docker-icon| to view the image on Docker Hub.
-
-.. list-table:: 
-    :header-rows: 1
-    :class: docker-image-compatibility
-
-    * - Docker image
-      - ROCm
-      - FlashInfer
-      - PyTorch
-      - Ubuntu
-      - Python
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/flashinfer/flashinfer-0.2.5_rocm6.4_ubuntu24.04_py3.12_pytorch2.7/images/sha256-558914838821c88c557fb6d42cfbc1bdb67d79d19759f37c764a9ee801f93313"><i class="fab fa-docker fa-lg"></i> rocm/flashinfer</a>
-      - `6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`__
-      - `v0.2.5 <https://github.com/flashinfer-ai/flashinfer/releases/tag/v0.2.5>`__
-      - `2.7.1 <https://github.com/ROCm/pytorch/releases/tag/v2.7.1>`__
-      - 24.04
-      - `3.12 <https://www.python.org/downloads/release/python-3129/>`__
-
-
@@ -2,7 +2,7 @@

 .. meta::
   :description: JAX compatibility
-   :keywords: GPU, JAX compatibility
+   :keywords: GPU, JAX, deep learning, framework compatibility

 .. version-set:: rocm_version latest

@@ -10,42 +10,61 @@
 JAX compatibility
 *******************************************************************************

-JAX provides a NumPy-like API, which combines automatic differentiation and the
-Accelerated Linear Algebra (XLA) compiler to achieve high-performance machine
-learning at scale.
+`JAX <https://docs.jax.dev/en/latest/notebooks/thinking_in_jax.html>`__ is a library 
+for array-oriented numerical computation (similar to NumPy), with automatic differentiation 
+and just-in-time (JIT) compilation to enable high-performance machine learning research.

-JAX uses composable transformations of Python and NumPy through just-in-time
-(JIT) compilation, automatic vectorization, and parallelization. To learn about
-JAX, including profiling and optimizations, see the official `JAX documentation
-<https://jax.readthedocs.io/en/latest/notebooks/quickstart.html>`_.
+JAX provides an API that combines automatic differentiation and the 
+Accelerated Linear Algebra (XLA) compiler to achieve high-performance machine 
+learning at scale. JAX uses composable transformations of Python and NumPy through 
+JIT compilation, automatic vectorization, and parallelization.

-ROCm support for JAX is upstreamed, and users can build the official source code
-with ROCm support:
+Support overview
+================================================================================

- ROCm JAX release:
+- The ROCm-supported version of JAX is maintained in the official `https://github.com/ROCm/rocm-jax 
+  <https://github.com/ROCm/rocm-jax>`__ repository, which differs from the 
+  `https://github.com/jax-ml/jax <https://github.com/jax-ml/jax>`__ upstream repository.

-  - Offers AMD-validated and community :ref:`Docker images <jax-docker-compat>`
-    with ROCm and JAX preinstalled.
+- To get started and install JAX on ROCm, use the prebuilt :ref:`Docker images <jax-docker-compat>`, 
+  which include ROCm, JAX, and all required dependencies.

-  - ROCm JAX repository: `ROCm/rocm-jax <https://github.com/ROCm/rocm-jax>`_
+  - See the :doc:`ROCm JAX installation guide <rocm-install-on-linux:install/3rd-party/jax-install>` 
+    for installation and setup instructions.

-  - See the :doc:`ROCm JAX installation guide <rocm-install-on-linux:install/3rd-party/jax-install>`
-    to get started.
+  - You can also consult the upstream `Installation guide <https://jax.readthedocs.io/en/latest/installation.html#amd-gpu-linux>`__ 
+    for additional context.

- Official JAX release:
+Version support
+--------------------------------------------------------------------------------

-  - Official JAX repository: `jax-ml/jax <https://github.com/jax-ml/jax>`_
+AMD releases official `ROCm JAX Docker images <https://hub.docker.com/r/rocm/jax/tags>`_
+quarterly alongside new ROCm releases. These images undergo full AMD testing.
+`Community ROCm JAX Docker images <https://hub.docker.com/r/rocm/jax-community/tags>`_
+follow upstream JAX releases and use the latest available ROCm version.

-  - See the `AMD GPU (Linux) installation section
-    <https://jax.readthedocs.io/en/latest/installation.html#amd-gpu-linux>`_ in
-    the JAX documentation.
+JAX Plugin-PJRT with JAX/JAXLIB compatibility
+================================================================================

-.. note::
+Portable JIT Runtime (PJRT) is an open, stable interface for device runtime and
+compiler. The following table details the ROCm version compatibility matrix
+between JAX Plugin–PJRT and JAX/JAXLIB.

-   AMD releases official `ROCm JAX Docker images <https://hub.docker.com/r/rocm/jax>`_
-   quarterly alongside new ROCm releases. These images undergo full AMD testing.
-   `Community ROCm JAX Docker images <https://hub.docker.com/r/rocm/jax-community>`_
-   follow upstream JAX releases and use the latest available ROCm version.
+.. list-table::
+    :header-rows: 1
+
+    * - JAX Plugin-PJRT
+      - JAX/JAXLIB
+      - ROCm
+    * - 0.8.0
+      - 0.8.0
+      - 7.2.0
+    * - 0.7.1
+      - 0.7.1
+      - 7.1.1, 7.1.0
+    * - 0.6.0
+      - 0.6.2, 0.6.0
+      - 7.0.2, 7.0.1, 7.0.0

 Use cases and recommendations
 ================================================================================
@@ -71,7 +90,7 @@ Use cases and recommendations
 * The `Distributed fine-tuning with JAX on AMD GPUs <https://rocm.blogs.amd.com/artificial-intelligence/distributed-sft-jax/README.html>`_
  outlines the process of fine-tuning a Bidirectional Encoder Representations
  from Transformers (BERT)-based large language model (LLM) using JAX for a text
-  classification task. The blog post discuss techniques for parallelizing the
+  classification task. The blog post discusses techniques for parallelizing the
  fine-tuning across multiple AMD GPUs and assess the model's performance on a
  holdout dataset. During the fine-tuning, a BERT-base-cased transformer model
  and the General Language Understanding Evaluation (GLUE) benchmark dataset was
@@ -90,9 +109,9 @@ For more use cases and recommendations, see `ROCm JAX blog posts <https://rocm.b
 Docker image compatibility
 ================================================================================

-AMD provides preconfigured Docker images with JAX and the ROCm backend.
-These images are published on `Docker Hub <https://hub.docker.com/r/rocm/jax>`__ and are the
-recommended way to get started with deep learning with JAX on ROCm.
+AMD validates and publishes `JAX images <https://hub.docker.com/r/rocm/jax/tags>`__
+with ROCm backends on Docker Hub.
+
 For ``jax-community`` images, see `rocm/jax-community
 <https://hub.docker.com/r/rocm/jax-community/tags>`__ on Docker Hub.

@@ -234,7 +253,7 @@ The ROCm supported data types in JAX are collected in the following table.

 .. note::

-  JAX data type support is effected by the :ref:`key_rocm_libraries` and it's
+  JAX data type support is affected by the :ref:`key_rocm_libraries` and it's
  collected on :doc:`ROCm data types and precision support <rocm:reference/precision-support>`
  page.

@@ -253,6 +272,33 @@ For a complete and up-to-date list of JAX public modules (for example, ``jax.num
  JAX API modules are maintained by the JAX project and is subject to change.
  Refer to the official Jax documentation for the most up-to-date information.

+Key features and enhancements for ROCm 7.1
+===============================================================================
+
+- Enabled compilation of multihost HLO runner Python bindings.
+
+  - Backported multihost HLO runner bindings and some related changes to
+    :code:`FunctionalHloRunner`.
+
+  - Added :code:`requirements_lock_3_12` to enable building for Python 3.12.
+
+- Removed hardcoded NHWC convolution layout for ``fp16`` precision to address the performance drops for ``fp16`` precision on gfx12xx GPUs.
+
+
+- ROCprofiler-SDK integration:
+
+  - Integrated ROCprofiler-SDK (v3) to XLA to improve profiling of GPU events,
+    support both time-based and step-based profiling.
+
+  - Added unit tests for :code:`rocm_collector` and :code:`rocm_tracer`.
+
+- Added Triton unsupported conversion from ``f8E4M3FNUZ`` to ``fp16`` with
+  rounding mode.
+
+- Introduced :code:`CudnnFusedConvDecomposer` to revert fused convolutions
+  when :code:`ConvAlgorithmPicker` fails to find a fused algorithm, and removed
+  unfused fallback paths from :code:`RocmFusedConvRunner`.
+
 Key features and enhancements for ROCm 7.0
 ===============================================================================

@@ -1,275 +0,0 @@
-:orphan:
-
-.. meta::
-    :description: llama.cpp deep learning framework compatibility
-    :keywords: GPU, GGML, llama.cpp compatibility
-
-.. version-set:: rocm_version latest
-
-********************************************************************************
-llama.cpp compatibility
-********************************************************************************
-
-`llama.cpp <https://github.com/ggml-org/llama.cpp>`__ is an open-source framework 
-for Large Language Model (LLM) inference that runs on both central processing units 
-(CPUs) and graphics processing units (GPUs). It is written in plain C/C++, providing 
-a simple, dependency-free setup. 
-
-The framework supports multiple quantization options, from 1.5-bit to 8-bit integers, 
-to accelerate inference and reduce memory usage. Originally built as a CPU-first library, 
-llama.cpp is easy to integrate with other programming environments and is widely 
-adopted across diverse platforms, including consumer devices. 
-
-ROCm support for llama.cpp is upstreamed, and you can build the official source code
-with ROCm support:
-
- ROCm support for llama.cpp is hosted in the official `https://github.com/ROCm/llama.cpp 
-  <https://github.com/ROCm/llama.cpp>`_ repository.
-
- Due to independent compatibility considerations, this location differs from the 
-  `https://github.com/ggml-org/llama.cpp <https://github.com/ggml-org/llama.cpp>`_ upstream repository.
-
- To install llama.cpp, use the prebuilt :ref:`Docker image <llama-cpp-docker-compat>`, 
-  which includes ROCm, llama.cpp, and all required dependencies.
-
-  - See the :doc:`ROCm llama.cpp installation guide <rocm-install-on-linux:install/3rd-party/llama-cpp-install>` 
-    to install and get started.
-
-  - See the `Installation guide <https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md#hip>`__ 
-    in the upstream llama.cpp documentation.
-
-.. note::
-
-  llama.cpp is supported on ROCm 7.0.0 and ROCm 6.4.x.
-
-Supported devices
-================================================================================
-
-**Officially Supported**: AMD Instinct™ MI300X, MI325X, MI210
-
-
-Use cases and recommendations
-================================================================================
-
-llama.cpp can be applied in a variety of scenarios, particularly when you need to meet one or more of the following requirements:
-
- Plain C/C++ implementation with no external dependencies
- Support for 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory usage
- Custom HIP (Heterogeneous-compute Interface for Portability) kernels for running large language models (LLMs) on AMD GPUs (graphics processing units)
- CPU (central processing unit) + GPU (graphics processing unit) hybrid inference for partially accelerating models larger than the total available VRAM (video random-access memory)
-
-llama.cpp is also used in a range of real-world applications, including:
-
- Games such as `Lucy's Labyrinth <https://github.com/MorganRO8/Lucys_Labyrinth>`__:
-  A simple maze game where AI-controlled agents attempt to trick the player.
- Tools such as `Styled Lines <https://marketplace.unity.com/packages/tools/ai-ml-integration/style-text-webgl-ios-stand-alone-llm-llama-cpp-wrapper-292902>`__:
-  A proprietary, asynchronous inference wrapper for Unity3D game development, including pre-built mobile and web platform wrappers and a model example.
- Various other AI applications use llama.cpp as their inference engine;  
-  for a detailed list, see the `user interfaces (UIs) section <https://github.com/ggml-org/llama.cpp?tab=readme-ov-file#description>`__.
-
-For more use cases and recommendations, refer to the `AMD ROCm blog <https://rocm.blogs.amd.com/>`__, 
-where you can search for llama.cpp examples and best practices to optimize your workloads on AMD GPUs.
-
- The `Llama.cpp Meets Instinct: A New Era of Open-Source AI Acceleration <https://rocm.blogs.amd.com/ecosystems-and-partners/llama-cpp/README.html>`__ 
-  blog post outlines how the open-source llama.cpp framework enables efficient LLM inference—including interactive inference with ``llama-cli``, 
-  server deployment with ``llama-server``, GGUF model preparation and quantization, performance benchmarking, and optimizations tailored for 
-  AMD Instinct GPUs within the ROCm ecosystem. 
-
-.. _llama-cpp-docker-compat:
-
-Docker image compatibility
-================================================================================
-
-.. |docker-icon| raw:: html
-
-   <i class="fab fa-docker"></i>
-
-AMD validates and publishes `ROCm llama.cpp Docker images <https://hub.docker.com/r/rocm/llama.cpp/tags>`__
-with ROCm backends on Docker Hub. The following Docker image tags and associated
-inventories represent the available llama.cpp versions from the official Docker Hub.
-Click |docker-icon| to view the image on Docker Hub.
-
-.. important::
-
-   Tag endings of ``_full``, ``_server``, and ``_light`` serve different purposes for entrypoints as follows:
-
-   - Full: This image includes both the main executable file and the tools to convert ``LLaMA`` models into ``ggml`` and convert into 4-bit quantization.
-   - Server: This image only includes the server executable file.
-   - Light: This image only includes the main executable file.
-
-.. list-table::
-    :header-rows: 1
-    :class: docker-image-compatibility
-
-    * - Full Docker
-      - Server Docker
-      - Light Docker
-      - llama.cpp
-      - ROCm
-      - Ubuntu
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm7.0.0_ubuntu24.04_full/images/sha256-a2ecd635eaa65bb289a9041330128677f3ae88bee6fee0597424b17e38d4903c"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
-      - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm7.0.0_ubuntu24.04_server/images/sha256-cb46b47df415addb5ceb6e6fdf0be70bf9d7f6863bbe6e10c2441ecb84246d52"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
-      - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm7.0.0_ubuntu24.04_light/images/sha256-8f8536eec4b05c0ff1c022f9fc6c527ad1c89e6c1ca0906e4d39e4de73edbde9"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
-      - `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
-      - `7.0.0 <https://repo.radeon.com/rocm/apt/7.0/>`__
-      - 24.04
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm7.0.0_ubuntu22.04_full/images/sha256-f36de2a3b03ae53e81c85422cb3780368c9891e1ac7884b04403a921fe2ea45d"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
-      - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm7.0.0_ubuntu22.04_server/images/sha256-df15e8ab11a6837cd3736644fec1e047465d49e37d610ab0b79df000371327df"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
-      - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm7.0.0_ubuntu22.04_light/images/sha256-4ea2d5bb7964f0ee3ea9b30ba7f343edd6ddfab1b1037669ca7eafad2e3c2bd7"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
-      - `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
-      - `7.0.0 <https://repo.radeon.com/rocm/apt/7.0/>`__
-      - 22.04
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.3_ubuntu24.04_full/images/sha256-5960fc850024a8a76451f9eaadd89b7e59981ae9f393b407310c1ddf18892577"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
-      - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.3_ubuntu24.04_server/images/sha256-1b79775d9f546065a6aaf9ca426e1dd4ed4de0b8f6ee83687758cc05af6538e6"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
-      - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.3_ubuntu24.04_light/images/sha256-8f863c4c2857ae42bebd64e4f1a0a1e7cc3ec4503f243e32b4a4dcad070ec361"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
-      - `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
-      - `6.4.3 <https://repo.radeon.com/rocm/apt/6.4.3/>`__
-      - 24.04
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.3_ubuntu22.04_full/images/sha256-888879b3ee208f9247076d7984524b8d1701ac72611689e89854a1588bec9867"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
-      - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.3_ubuntu22.04_server/images/sha256-90e4ff99a66743e33fd00728cd71a768588e5f5ef355aaa196669fe65ac70672"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
-      - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.3_ubuntu22.04_light/images/sha256-bd447a049939cb99054f8fbf3f2352870fe906a75e2dc3339c845c08b9c53f9b"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
-      - `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
-      - `6.4.3 <https://repo.radeon.com/rocm/apt/6.4.3/>`__
-      - 22.04
-
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.2_ubuntu24.04_full/images/sha256-5b3a1bc4889c1fcade434b937fbf9cc1c22ff7dc0317c130339b0c9238bc88c4"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
-      - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.2_ubuntu24.04_server/images/sha256-5228ff99d0f627a9032d668f4381b2e80dc1e301adc3e0821f26d8354b175271"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
-      - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.2_ubuntu24.04_light/images/sha256-b12723b332a826a89b7252dddf868cbe4d1a869562fc4aa4032f59e1a683b968"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
-      - `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
-      - `6.4.2 <https://repo.radeon.com/rocm/apt/6.4.2/>`__
-      - 24.04
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.2_ubuntu22.04_full/images/sha256-cd6e21a6a73f59b35dd5309b09dd77654a94d783bf13a55c14eb8dbf8e9c2615"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
-      - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.2_ubuntu22.04_server/images/sha256-c2b4689ab2c47e6626e8fea22d7a63eb03d47c0fde9f5ef8c9f158d15c423e58"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
-      - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.2_ubuntu22.04_light/images/sha256-1acc28f29ed87db9cbda629cb29e1989b8219884afe05f9105522be929e94da4"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
-      - `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
-      - `6.4.2 <https://repo.radeon.com/rocm/apt/6.4.2/>`__
-      - 22.04
-
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.1_ubuntu24.04_full/images/sha256-2f8ae8a44510d96d52dea6cb398b224f7edeb7802df7ec488c6f63d206b3cdc9"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
-      - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.1_ubuntu24.04_server/images/sha256-fece497ff9f4a28b12f645de52766941da8ead8471aa1ea84b61d4b4568e51f2"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
-      - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.1_ubuntu24.04_light/images/sha256-3e14352fa6f8c6128b23cf9342531c20dbfb522550b626e09d83b260a1947022"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
-      - `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
-      - `6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`__
-      - 24.04
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.1_ubuntu22.04_full/images/sha256-80763062ef0bec15038c35fd01267f1fc99a5dd171d4b48583cc668b15efad69"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
-      - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.1_ubuntu22.04_server/images/sha256-db2a6c957555ed83b819bbc54aea884a93192da0fb512dae63d32e0dc4e8ab8f"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
-      - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.1_ubuntu22.04_light/images/sha256-c6dbb07cc655fb079d5216e4b77451cb64a9daa0585d23b6fb8b32cb22021197"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
-      - `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
-      - `6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`__
-      - 22.04
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b5997_rocm6.4.0_ubuntu24.04_full/images/sha256-f78f6c81ab2f8e957469415fe2370a1334fe969c381d1fe46050c85effaee9d5"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
-      - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b5997_rocm6.4.0_ubuntu24.04_server/images/sha256-275ad9e18f292c26a00a2de840c37917e98737a88a3520bdc35fd3fc5c9a6a9b"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
-      - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b5997_rocm6.4.0_ubuntu24.04_light/images/sha256-cc324e6faeedf0e400011f07b49d2dc41a16bae257b2b7befa0f4e2e97231320"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
-      - `b5997 <https://github.com/ROCm/llama.cpp/tree/release/b5997>`__
-      - `6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`__
-      - 24.04
-
-
-Key ROCm libraries for llama.cpp
-================================================================================
-
-llama.cpp functionality on ROCm is determined by its underlying library
-dependencies. These ROCm components affect the capabilities, performance, and
-feature set available to developers. Ensure you have the required libraries for 
-your corresponding ROCm version.
-
-.. list-table::
-    :header-rows: 1
-
-    * - ROCm library
-      - ROCm 7.0.0 version
-      - ROCm 6.4.x version
-      - Purpose
-      - Usage
-    * - `hipBLAS <https://github.com/ROCm/hipBLAS>`__
-      - 3.0.0
-      - 2.4.0
-      - Provides GPU-accelerated Basic Linear Algebra Subprograms (BLAS) for
-        matrix and vector operations.
-      - Supports operations such as matrix multiplication, matrix-vector
-        products, and tensor contractions. Utilized in both dense and batched
-        linear algebra operations.
-    * - `hipBLASLt <https://github.com/ROCm/hipBLASLt>`__
-      - 1.0.0
-      - 0.12.0
-      - hipBLASLt is an extension of the hipBLAS library, providing additional
-        features like epilogues fused into the matrix multiplication kernel or
-        use of integer tensor cores.
-      - By setting the flag ``ROCBLAS_USE_HIPBLASLT``, you can dispatch hipblasLt
-        kernels where possible.
-    * - `rocWMMA <https://github.com/ROCm/rocWMMA>`__
-      - 2.0.0
-      - 1.7.0
-      - Accelerates warp-level matrix-multiply and matrix-accumulate to speed up matrix
-        multiplication (GEMM) and accumulation operations with mixed precision
-        support.
-      - Can be used to enhance the flash attention performance on AMD compute, by enabling
-        the flag during compile time.
-
-Previous versions
-===============================================================================
-See :doc:`rocm-install-on-linux:install/3rd-party/previous-versions/llama-cpp-history` to find documentation for previous releases
-of the ``ROCm/llama.cpp`` Docker image.
@@ -1,93 +0,0 @@
-:orphan:
-
-.. meta::
-    :description: Megablocks compatibility
-    :keywords: GPU, megablocks, compatibility
-
-.. version-set:: rocm_version latest
-
-********************************************************************************
-Megablocks compatibility
-********************************************************************************
-
-Megablocks is a light-weight library for mixture-of-experts (MoE) training. 
-The core of the system is efficient "dropless-MoE" and standard MoE layers. 
-Megablocks is integrated with `https://github.com/stanford-futuredata/Megatron-LM <https://github.com/stanford-futuredata/Megatron-LM>`_, 
-where data and pipeline parallel training of MoEs is supported.
-
-* ROCm support for Megablocks is hosted in the official `https://github.com/ROCm/megablocks <https://github.com/ROCm/megablocks>`_ repository. 
-* Due to independent compatibility considerations, this location differs from the `https://github.com/stanford-futuredata/Megatron-LM <https://github.com/stanford-futuredata/Megatron-LM>`_ upstream repository. 
-* Use the prebuilt :ref:`Docker image <megablocks-docker-compat>` with ROCm, PyTorch, and Megablocks preinstalled. 
-* See the :doc:`ROCm Megablocks installation guide <rocm-install-on-linux:install/3rd-party/megablocks-install>` to install and get started.
-
-.. note::
-
-  Megablocks is supported on ROCm 6.3.0.
-
-Supported devices
-================================================================================
-
- **Officially Supported**: AMD Instinct MI300X
- **Partially Supported** (functionality or performance limitations): AMD Instinct MI250X, MI210
-
-Supported models and features
-================================================================================
-
-This section summarizes the Megablocks features supported by ROCm.
-
-* Distributed Pre-training
-* Activation Checkpointing and Recomputation
-* Distributed Optimizer
-* Mixture-of-Experts
-* dropless-Mixture-of-Experts
-
-
-.. _megablocks-recommendations:
-
-Use cases and recommendations
-================================================================================
-
-The `ROCm Megablocks blog posts <https://rocm.blogs.amd.com/artificial-intelligence/megablocks/README.html>`_ 
-guide how to leverage the ROCm platform for pre-training using the Megablocks framework. 
-It features how to pre-process datasets and how to begin pre-training on AMD GPUs through:
-
-* Single-GPU pre-training
-* Multi-GPU pre-training
-
-
-.. _megablocks-docker-compat:
-
-Docker image compatibility
-================================================================================
-
-.. |docker-icon| raw:: html
-
-   <i class="fab fa-docker"></i>
-
-AMD validates and publishes `ROCm Megablocks images <https://hub.docker.com/r/rocm/megablocks/tags>`_
-with ROCm and Pytorch backends on Docker Hub. The following Docker image tags and associated
-inventories represent the latest Megatron-LM version from the official Docker Hub.
-The Docker images have been validated for `ROCm 6.3.0 <https://repo.radeon.com/rocm/apt/6.3/>`_.
-Click |docker-icon| to view the image on Docker Hub.
-
-.. list-table:: 
-    :header-rows: 1
-    :class: docker-image-compatibility
-
-    * - Docker image
-      - ROCm
-      - Megablocks
-      - PyTorch
-      - Ubuntu
-      - Python
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/megablocks/megablocks-0.7.0_rocm6.3.0_ubuntu24.04_py3.12_pytorch2.4.0/images/sha256-372ff89b96599019b8f5f9db469c84add2529b713456781fa62eb9a148659ab4"><i class="fab fa-docker fa-lg"></i> rocm/megablocks</a>
-      - `6.3.0 <https://repo.radeon.com/rocm/apt/6.3/>`_
-      - `0.7.0 <https://github.com/databricks/megablocks/releases/tag/v0.7.0>`_
-      - `2.4.0 <https://github.com/ROCm/pytorch/tree/release/2.4>`_
-      - 24.04
-      - `3.12.9 <https://www.python.org/downloads/release/python-3129/>`_
-
-
@@ -2,7 +2,7 @@

 .. meta::
    :description: PyTorch compatibility
-    :keywords: GPU, PyTorch compatibility
+    :keywords: GPU, PyTorch, deep learning, framework compatibility

 .. version-set:: rocm_version latest

@@ -15,40 +15,42 @@ deep learning. PyTorch on ROCm provides mixed-precision and large-scale training
 using `MIOpen <https://github.com/ROCm/MIOpen>`__ and
 `RCCL <https://github.com/ROCm/rccl>`__ libraries.

-ROCm support for PyTorch is upstreamed into the official PyTorch repository. Due
-to independent compatibility considerations, this results in two distinct
-release cycles for PyTorch on ROCm:
+PyTorch provides two high-level features:

- ROCm PyTorch release:
+- Tensor computation (like NumPy) with strong GPU acceleration

-  - Provides the latest version of ROCm but might not necessarily support the
-    latest stable PyTorch version.
+- Deep neural networks built on a tape-based autograd system (rapid computation 
+  of multiple partial derivatives or gradients)

-  - Offers :ref:`Docker images <pytorch-docker-compat>` with ROCm and PyTorch
-    preinstalled.
+Support overview
+================================================================================

-  - ROCm PyTorch repository: `<https://github.com/ROCm/pytorch>`__
+ROCm support for PyTorch is upstreamed into the official PyTorch repository. 
+ROCm development is aligned with the stable release of PyTorch, while upstream 
+PyTorch testing uses the stable release of ROCm to maintain consistency:

-  - See the :doc:`ROCm PyTorch installation guide <rocm-install-on-linux:install/3rd-party/pytorch-install>`
-    to get started.
+- The ROCm-supported version of PyTorch is maintained in the official `https://github.com/ROCm/pytorch 
+  <https://github.com/ROCm/pytorch>`__ repository, which differs from the 
+  `https://github.com/pytorch/pytorch <https://github.com/pytorch/pytorch>`__ upstream repository.

- Official PyTorch release:
+- To get started and install PyTorch on ROCm, use the prebuilt :ref:`Docker images <pytorch-docker-compat>`, 
+  which include ROCm, PyTorch, and all required dependencies.

-  - Provides the latest stable version of PyTorch  but might not necessarily
-    support the latest ROCm version.
+  - See the :doc:`ROCm PyTorch installation guide <rocm-install-on-linux:install/3rd-party/pytorch-install>` 
+    for installation and setup instructions.

-  - Official PyTorch repository: `<https://github.com/pytorch/pytorch>`__
-
-  - See the `Nightly and latest stable version installation guide <https://pytorch.org/get-started/locally/>`__
-    or `Previous versions <https://pytorch.org/get-started/previous-versions/>`__
-    to get started.
+  - You can also consult the upstream `Installation guide <https://pytorch.org/get-started/locally/>`__ or 
+    `Previous versions <https://pytorch.org/get-started/previous-versions/>`__ for additional context.

 PyTorch includes tooling that generates HIP source code from the CUDA backend.
 This approach allows PyTorch to support ROCm without requiring manual code
 modifications. For more information, see :doc:`HIPIFY <hipify:index>`.

-ROCm development is aligned with the stable release of PyTorch, while upstream
-PyTorch testing uses the stable release of ROCm to maintain consistency.
+Version support
+--------------------------------------------------------------------------------
+
+AMD releases official `ROCm PyTorch Docker images <https://hub.docker.com/r/rocm/pytorch/tags>`_
+quarterly alongside new ROCm releases. These images undergo full AMD testing.

 .. _pytorch-recommendations:

@@ -78,7 +80,7 @@ Use cases and recommendations
  GPU.

 * The :doc:`Inception with PyTorch documentation </conceptual/ai-pytorch-inception>`
-  describes how PyTorch integrates with ROCm for AI workloads It outlines the
+  describes how PyTorch integrates with ROCm for AI workloads. It outlines the
  use of PyTorch on the ROCm platform and focuses on efficiently leveraging AMD
  GPU hardware for training and inference tasks in AI applications.

@@ -89,9 +91,8 @@ For more use cases and recommendations, see `ROCm PyTorch blog posts <https://ro
 Docker image compatibility
 ================================================================================

-AMD provides preconfigured Docker images with PyTorch and the ROCm backend.
-These images are published on `Docker Hub <https://hub.docker.com/r/rocm/pytorch>`__ and are the
-recommended way to get started with deep learning with PyTorch on ROCm.
+AMD validates and publishes `PyTorch images <https://hub.docker.com/r/rocm/pytorch/tags>`__
+with ROCm backends on Docker Hub.

 To find the right image tag, see the :ref:`PyTorch on ROCm installation
 documentation <rocm-install-on-linux:pytorch-docker-support>` for a list of
@@ -348,7 +349,7 @@ with ROCm.
        you need to explicitly move audio data (waveform tensor) to GPU using
        ``.to('cuda')``.

-    * - `torchtune <https://docs.pytorch.org/torchtune/stable/index.html>`_
+    * - `torchtune <https://meta-pytorch.org/torchtune/stable/index.html>`_
      - PyTorch-native library designed for fine-tuning large language models
        (LLMs). Provides supports the full fine-tuning workflow and offers
        compatibility with popular production inference systems.
@@ -360,21 +361,12 @@ with ROCm.
        popular datasets, model architectures, and common image transformations
        for computer vision applications.

-    * - `torchtext <https://docs.pytorch.org/text/stable/index.html>`_
-      - Text processing library for PyTorch. Provides data processing utilities
-        and popular datasets for natural language processing, including
-        tokenization, vocabulary management, and text embeddings.
-
-        **Note:** ``torchtext`` does not implement ROCm-specific kernels.
-        ROCm acceleration is provided through the underlying PyTorch framework
-        and ROCm library integration. Only official release exists.
-
    * - `torchdata <https://meta-pytorch.org/data/beta/index.html#torchdata>`_
      - Beta library of common modular data loading primitives for easily
        constructing flexible and performant data pipelines, with features still
        in prototype stage.

-    * - `torchrec <https://docs.pytorch.org/torchrec/>`_
+    * - `torchrec <https://meta-pytorch.org/torchrec/>`_
      - PyTorch domain library for common sparsity and parallelism primitives
        needed for large-scale recommender systems, enabling authors to train
        models with large embedding tables shared across many GPUs.
@@ -407,7 +399,40 @@ with ROCm.

        **Note:** Only official release exists.

-Key features and enhancements for PyTorch 2.7 with ROCm 7.0
+Key features and enhancements for PyTorch 2.9 with ROCm 7.1.1
+================================================================================
+- Scaled Dot Product Attention (SDPA) upgraded to use AOTriton version 0.11b.
+
+- Default hipBLASLt support enabled for gfx908 architecture on ROCm 6.3 and later.
+
+- MIOpen now supports channels last memory format for 3D convolutions and batch normalization.
+
+- NHWC convolution operations in MIOpen optimized by eliminating unnecessary transpose operations.
+
+- Improved tensor.item() performance by removing redundant synchronization.
+
+- Enhanced performance for element-wise operations and reduction kernels.
+
+- Added support for grouped GEMM operations through fbgemm_gpu generative AI components.
+
+- Resolved device error in Inductor when using CUDA graph trees with HIP.
+
+- Corrected logsumexp scaling in AOTriton-based SDPA implementation.
+
+- Added stream graph capture status validation in memory copy synchronization functions.
+
+Key features and enhancements for PyTorch 2.8 with ROCm 7.1
+================================================================================
+
+- MIOpen deep learning optimizations: Further optimized NHWC BatchNorm feature.
+
+- Added float8 support for the DeepSpeed extension, allowing for decreased
+  memory footprint and increased throughput in training and inference workloads.
+
+- ``torch.nn.functional.scaled_dot_product_attention`` now calling optimized
+  flash attention kernel automatically.
+
+Key features and enhancements for PyTorch 2.7/2.8 with ROCm 7.0
 ================================================================================

 - Enhanced TunableOp framework: Introduces ``tensorfloat32`` support for
@@ -442,10 +467,6 @@ Key features and enhancements for PyTorch 2.7 with ROCm 7.0
  ROCm-specific test conditions, and enhanced unit test coverage for Flash
  Attention and Memory Efficient operations.

- Build system and infrastructure improvements: Provides updated CentOS Stream 9
-  support, improved Docker configuration, migration to public MAGMA repository,
-  and enhanced QA automation scripts for PyTorch unit testing.
-
 - Composable Kernel (CK) updates: Features updated CK submodule integration with
  the latest optimizations and performance improvements for core mathematical
  operations.
@@ -467,7 +488,7 @@ Key features and enhancements for PyTorch 2.7 with ROCm 7.0
  network training or inference. For AMD platforms, ``amdclang++`` has been
  validated as the supported compiler for building these extensions.

-Known issues and notes for PyTorch 2.7 with ROCm 7.0
+Known issues and notes for PyTorch 2.7/2.8 with ROCm 7.0 and ROCm 7.1
 ================================================================================

 - The ``matmul.allow_fp16_reduced_precision_reduction`` and
@@ -1,111 +0,0 @@
-:orphan:
-
-.. meta::
-    :description: Ray deep learning framework compatibility
-    :keywords: GPU, Ray compatibility
-
-.. version-set:: rocm_version latest
-
-*******************************************************************************
-Ray compatibility
-*******************************************************************************
-
-Ray is a unified framework for scaling AI and Python applications from your laptop 
-to a full cluster, without changing your code. Ray consists of `a core distributed 
-runtime  <https://docs.ray.io/en/latest/ray-core/walkthrough.html>`_ and a set of 
-`AI libraries <https://docs.ray.io/en/latest/ray-air/getting-started.html>`_ for 
-simplifying machine learning computations.
-
-Ray is a general-purpose framework that runs many types of workloads efficiently. 
-Any Python application can be scaled with Ray, without extra infrastructure.
-
-ROCm support for Ray is upstreamed, and you can build the official source code
-with ROCm support: 
-
- ROCm support for Ray is hosted in the official `https://github.com/ROCm/ray 
-  <https://github.com/ROCm/ray>`_ repository.
-
- Due to independent compatibility considerations, this location differs from the 
-  `https://github.com/ray-project/ray <https://github.com/ray-project/ray>`_ upstream repository.
-
- To install Ray, use the prebuilt :ref:`Docker image <ray-docker-compat>` 
-  which includes ROCm, Ray, and all required dependencies.
-
-  - See the :doc:`ROCm Ray installation guide <rocm-install-on-linux:install/3rd-party/ray-install>` 
-    for instructions to get started.
-
-  - See the `Installation section <https://docs.ray.io/en/latest/ray-overview/installation.html>`_ 
-    in the upstream Ray documentation.
-
-  - The Docker image provided is based on the upstream Ray `Daily Release (Nightly) wheels <https://docs.ray.io/en/latest/ray-overview/installation.html#daily-releases-nightlies>`__ 
-    corresponding to commit `005c372 <https://github.com/ray-project/ray/commit/005c372262e050d5745f475e22e64305fa07f8b8>`__.
-
-.. note::
-
-  Ray is supported on ROCm 6.4.1.
-
-Supported devices
-================================================================================
-
-**Officially Supported**: AMD Instinct™ MI300X, MI210
-
-
-Use cases and recommendations
-================================================================================
-
-* The `Reinforcement Learning from Human Feedback on AMD GPUs with verl and ROCm 
-  Integration <https://rocm.blogs.amd.com/artificial-intelligence/verl-large-scale/README.html>`__  
-  blog provides an overview of Volcano Engine Reinforcement Learning (verl) 
-  for large language models (LLMs) and discusses its benefits in large-scale 
-  reinforcement learning from human feedback (RLHF). It uses Ray as part of a 
-  hybrid orchestration engine to schedule and coordinate training and inference 
-  tasks in parallel, enabling optimized resource utilization and potential overlap 
-  between these phases. This dynamic resource allocation strategy significantly 
-  improves overall system efficiency. The blog presents verl’s performance results, 
-  focusing on throughput and convergence accuracy achieved on AMD Instinct™ MI300X 
-  GPUs. Follow this guide to get started with verl on AMD Instinct GPUs and 
-  accelerate your RLHF training with ROCm-optimized performance.
-
-* The `Exploring Use Cases for Scalable AI: Implementing Ray with ROCm Support for Efficient ML Workflows 
-  <https://rocm.blogs.amd.com/artificial-intelligence/rocm-ray/README.html>`__
-  blog post describes key use cases such as training and inference for large language models (LLMs), 
-  model serving, hyperparameter tuning, reinforcement learning, and the orchestration of large-scale 
-  workloads using Ray in the ROCm environment.
-
-For more use cases and recommendations, see the AMD GPU tabs in the `Accelerator Support 
-topic <https://docs.ray.io/en/latest/ray-core/scheduling/accelerators.html#accelerator-support>`__ 
-of the Ray core documentation and refer to the `AMD ROCm blog <https://rocm.blogs.amd.com/>`__, 
-where you can search for Ray examples and best practices to optimize your workloads on AMD GPUs.
-
-.. _ray-docker-compat:
-
-Docker image compatibility
-================================================================================
-
-.. |docker-icon| raw:: html
-
-   <i class="fab fa-docker"></i>
-
-AMD validates and publishes ready-made `ROCm Ray Docker images <https://hub.docker.com/r/rocm/ray/tags>`__
-with ROCm backends on Docker Hub. The following Docker image tags and
-associated inventories represent the latest Ray version from the official Docker Hub and are validated for
-`ROCm 6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`_. Click the |docker-icon|
-icon to view the image on Docker Hub.
-
-.. list-table::
-    :header-rows: 1
-    :class: docker-image-compatibility
-
-    * - Docker image
-      - Ray
-      - Pytorch
-      - Ubuntu
-      - Python
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/ray/ray-2.48.0.post0_rocm6.4.1_ubuntu24.04_py3.12_pytorch2.6.0/images/sha256-0d166fe6bdced38338c78eedfb96eff92655fb797da3478a62dd636365133cc0"><i class="fab fa-docker fa-lg"></i> rocm/ray</a>
-      - `2.48.0.post0 <https://github.com/ROCm/ray/tree/release/2.48.0.post0>`_
-      - 2.6.0+git684f6f2
-      - 24.04
-      - `3.12.10 <https://www.python.org/downloads/release/python-31210/>`_
@@ -1,100 +0,0 @@
-:orphan:
-
-.. meta::
-    :description: Stanford Megatron-LM compatibility
-    :keywords: Stanford, Megatron-LM, compatibility
-
-.. version-set:: rocm_version latest
-
-********************************************************************************
-Stanford Megatron-LM compatibility
-********************************************************************************
-
-Stanford Megatron-LM is a large-scale language model training framework developed by NVIDIA `https://github.com/NVIDIA/Megatron-LM <https://github.com/NVIDIA/Megatron-LM>`_. It is
-designed to train massive transformer-based language models efficiently by model and data parallelism. 
-
-* ROCm support for Stanford Megatron-LM is hosted in the official `https://github.com/ROCm/Stanford-Megatron-LM <https://github.com/ROCm/Stanford-Megatron-LM>`_ repository. 
-* Due to independent compatibility considerations, this location differs from the `https://github.com/stanford-futuredata/Megatron-LM <https://github.com/stanford-futuredata/Megatron-LM>`_ upstream repository. 
-* Use the prebuilt :ref:`Docker image <megatron-lm-docker-compat>` with ROCm, PyTorch, and Megatron-LM preinstalled. 
-* See the :doc:`ROCm Stanford Megatron-LM installation guide <rocm-install-on-linux:install/3rd-party/stanford-megatron-lm-install>` to install and get started.
-
-.. note::
-
-	Stanford Megatron-LM is supported on ROCm 6.3.0.
-
-
-Supported Devices
-================================================================================
-
- **Officially Supported**: AMD Instinct MI300X
- **Partially Supported** (functionality or performance limitations): AMD Instinct MI250X, MI210
-
-
-Supported models and features
-================================================================================
-
-This section details models & features that are supported by the ROCm version on Stanford Megatron-LM.
-
-Models:
-
-* Bert
-* GPT
-* T5
-* ICT
-
-Features:
-
-* Distributed Pre-training
-* Activation Checkpointing and Recomputation
-* Distributed Optimizer
-* Mixture-of-Experts
-
-.. _megatron-lm-recommendations:
-
-Use cases and recommendations
-================================================================================
-
-See the `Efficient MoE training on AMD ROCm: How-to use Megablocks on AMD GPUs blog <https://rocm.blogs.amd.com/artificial-intelligence/megablocks/README.html>`_ post  
-to leverage the ROCm platform for pre-training by using the Stanford Megatron-LM framework of pre-processing datasets on AMD GPUs. 
-Coverage includes:
-
-  * Single-GPU pre-training
-  * Multi-GPU pre-training
-
-
-.. _megatron-lm-docker-compat:
-
-Docker image compatibility
-================================================================================
-
-.. |docker-icon| raw:: html
-
-   <i class="fab fa-docker"></i>
-
-AMD validates and publishes `Stanford Megatron-LM images <https://hub.docker.com/r/rocm/megatron-lm>`_
-with ROCm and Pytorch backends on Docker Hub. The following Docker image tags and associated
-inventories represent the latest Megatron-LM version from the official Docker Hub.
-The Docker images have been validated for `ROCm 6.3.0 <https://repo.radeon.com/rocm/apt/6.3/>`_.
-Click |docker-icon| to view the image on Docker Hub.
-
-.. list-table:: 
-    :header-rows: 1
-    :class: docker-image-compatibility
-
-    * - Docker image
-      - Stanford Megatron-LM
-      - PyTorch
-      - Ubuntu
-      - Python
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/stanford-megatron-lm/stanford-megatron-lm85f95ae_rocm6.3.0_ubuntu24.04_py3.12_pytorch2.4.0/images/sha256-070556f078be10888a1421a2cb4f48c29f28b02bfeddae02588d1f7fc02a96a6"><i class="fab fa-docker fa-lg"></i></a>
-
-      - `85f95ae <https://github.com/stanford-futuredata/Megatron-LM/commit/85f95aef3b648075fe6f291c86714fdcbd9cd1f5>`_
-      - `2.4.0 <https://github.com/ROCm/pytorch/tree/release/2.4>`_
-      - 24.04
-      - `3.12.9 <https://www.python.org/downloads/release/python-3129/>`_
-
-      
-
@@ -1,76 +0,0 @@
-:orphan:
-
-.. meta::
-    :description: Taichi compatibility
-    :keywords: GPU, Taichi compatibility
-
-.. version-set:: rocm_version latest
-
-*******************************************************************************
-Taichi compatibility
-*******************************************************************************
-
-`Taichi <https://www.taichi-lang.org/>`_ is an open-source, imperative, and parallel 
-programming language designed for high-performance numerical computation. 
-Embedded in Python, it leverages just-in-time (JIT) compilation frameworks such as LLVM to accelerate 
-compute-intensive Python code by compiling it to native GPU or CPU instructions.
-
-Taichi is widely used across various domains, including real-time physical simulation, 
-numerical computing, augmented reality, artificial intelligence, computer vision, robotics, 
-visual effects in film and gaming, and general-purpose computing.
-
-* ROCm support for Taichi is hosted in the official `https://github.com/ROCm/taichi <https://github.com/ROCm/taichi>`_ repository.
-* Due to independent compatibility considerations, this location differs from the `https://github.com/taichi-dev <https://github.com/taichi-dev>`_ upstream repository.
-* Use the prebuilt :ref:`Docker image <taichi-docker-compat>` with ROCm, PyTorch, and Taichi preinstalled.
-* See the :doc:`ROCm Taichi installation guide <rocm-install-on-linux:install/3rd-party/taichi-install>` to install and get started.
-
-.. note::
-
-	Taichi is supported on ROCm 6.3.2.
-
-Supported devices and features
-===============================================================================
-There is support through the ROCm software stack for all Taichi GPU features on AMD Instinct MI250X and MI210X Series GPUs with the exception of Taichi’s GPU rendering system, CGUI.
-AMD Instinct MI300X Series GPUs will be supported by November.
-
-.. _taichi-recommendations:
-
-Use cases and recommendations
-================================================================================
-To fully leverage Taichi's performance capabilities in compute-intensive tasks, it is best to adhere to specific coding patterns and utilize Taichi decorators. 
-A collection of example use cases is available in the `https://github.com/ROCm/taichi_examples <https://github.com/ROCm/taichi_examples>`_ repository, 
-providing practical insights and foundational knowledge for working with the Taichi programming language. 
-You can also refer to the `AMD ROCm blog <https://rocm.blogs.amd.com/>`_ to search for Taichi examples and best practices to optimize your workflows on AMD GPUs.
-
-.. _taichi-docker-compat:
-
-Docker image compatibility
-================================================================================
-
-.. |docker-icon| raw:: html
-
-   <i class="fab fa-docker"></i>
-
-AMD validates and publishes ready-made `ROCm Taichi Docker images <https://hub.docker.com/r/rocm/taichi/tags>`_
-with ROCm backends on Docker Hub. The following Docker image tags and associated inventories 
-represent the latest Taichi version from the official Docker Hub.
-The Docker images have been validated for `ROCm 6.3.2 <https://rocm.docs.amd.com/en/docs-6.3.2/about/release-notes.html>`_. 
-Click |docker-icon| to view the image on Docker Hub.
-
-.. list-table:: 
-    :header-rows: 1
-    :class: docker-image-compatibility
-
-    * - Docker image
-      - ROCm
-      - Taichi
-      - Ubuntu
-      - Python
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/taichi/taichi-1.8.0b1_rocm6.3.2_ubuntu22.04_py3.10.12/images/sha256-e016964a751e6a92199032d23e70fa3a564fff8555afe85cd718f8aa63f11fc6"><i class="fab fa-docker fa-lg"></i> rocm/taichi</a>
-      - `6.3.2 <https://repo.radeon.com/rocm/apt/6.3.2/>`_
-      - `1.8.0b1 <https://github.com/taichi-dev/taichi>`_
-      - 22.04
-      - `3.10.12 <https://www.python.org/downloads/release/python-31012/>`_
@@ -2,7 +2,7 @@

 .. meta::
    :description: TensorFlow compatibility
-    :keywords: GPU, TensorFlow compatibility
+    :keywords: GPU, TensorFlow, deep learning, framework compatibility

 .. version-set:: rocm_version latest

@@ -12,37 +12,33 @@ TensorFlow compatibility

 `TensorFlow <https://www.tensorflow.org/>`__ is an open-source library for
 solving machine learning, deep learning, and AI problems. It can solve many
-problems across different sectors and industries but primarily focuses on
-neural network training and inference. It is one of the most popular and
-in-demand frameworks and is very active in open-source contribution and
-development.
+problems across different sectors and industries, but primarily focuses on
+neural network training and inference. It is one of the most popular deep 
+learning frameworks and is very active in open-source development.
+
+Support overview
+================================================================================
+
+- The ROCm-supported version of TensorFlow is maintained in the official `https://github.com/ROCm/tensorflow-upstream 
+  <https://github.com/ROCm/tensorflow-upstream>`__ repository, which differs from the 
+  `https://github.com/tensorflow/tensorflow <https://github.com/tensorflow/tensorflow>`__ upstream repository.
+
+- To get started and install TensorFlow on ROCm, use the prebuilt :ref:`Docker images <tensorflow-docker-compat>`, 
+  which include ROCm, TensorFlow, and all required dependencies.
+
+  - See the :doc:`ROCm TensorFlow installation guide <rocm-install-on-linux:install/3rd-party/tensorflow-install>` 
+    for installation and setup instructions.
+
+  - You can also consult the `TensorFlow API versions <https://www.tensorflow.org/versions>`__ list 
+    for additional context.
+
+Version support
+--------------------------------------------------------------------------------

 The `official TensorFlow repository <http://github.com/tensorflow/tensorflow>`__
 includes full ROCm support. AMD maintains a TensorFlow `ROCm repository
 <http://github.com/rocm/tensorflow-upstream>`__ in order to quickly add bug
-fixes, updates, and support for the latest ROCM versions.
-
- ROCm TensorFlow release:
-
-  - Offers :ref:`Docker images <tensorflow-docker-compat>` with
-    ROCm and TensorFlow pre-installed.
-
-  - ROCm TensorFlow repository: `<https://github.com/ROCm/tensorflow-upstream>`__
-
-  - See the :doc:`ROCm TensorFlow installation guide <rocm-install-on-linux:install/3rd-party/tensorflow-install>`
-    to get started.
-
- Official TensorFlow release:
-
-  - Official TensorFlow repository: `<https://github.com/tensorflow/tensorflow>`__
-
-  - See the `TensorFlow API versions <https://www.tensorflow.org/versions>`__ list.
-
-  .. note::
-
-     The official TensorFlow documentation does not cover ROCm support. Use the
-     ROCm documentation for installation instructions for Tensorflow on ROCm.
-     See :doc:`rocm-install-on-linux:install/3rd-party/tensorflow-install`.
+fixes, updates, and support for the latest ROCm versions.

 .. _tensorflow-docker-compat:

@@ -140,7 +136,7 @@ The following section maps supported data types and GPU-accelerated TensorFlow
 features to their minimum supported ROCm and TensorFlow versions.

 Data types
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+---------------

 The data type of a tensor is specified using the ``dtype`` attribute or
 argument, and TensorFlow supports a wide range of data types for different use
@@ -258,7 +254,7 @@ are as follows:
      - 1.7

 Features
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+---------------

 This table provides an overview of key features in TensorFlow and their
 availability in ROCm.
@@ -350,7 +346,7 @@ availability in ROCm.
      - 1.9.2

 Distributed library features
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+-----------------------------------

 Enables developers to scale computations across multiple devices on a single machine or
 across multiple machines.
@@ -1,86 +0,0 @@
-:orphan:
-
-.. meta::
-   :description: verl compatibility
-   :keywords: GPU, verl compatibility
-
-.. version-set:: rocm_version latest
-
-*******************************************************************************
-verl compatibility
-*******************************************************************************
-
-Volcano Engine Reinforcement Learning for LLMs (verl) is a reinforcement learning framework designed for large language models (LLMs). 
-verl offers a scalable, open-source fine-tuning solution optimized for AMD Instinct GPUs with full ROCm support.
-
-* See the `verl documentation <https://verl.readthedocs.io/en/latest/>`_ for more information about verl. 
-* The official verl GitHub repository is `https://github.com/volcengine/verl <https://github.com/volcengine/verl>`_.
-* Use the AMD-validated :ref:`Docker images <verl-docker-compat>` with ROCm and verl preinstalled. 
-* See the :doc:`ROCm verl installation guide <rocm-install-on-linux:install/3rd-party/verl-install>` to install and get started.
-
-.. note::
-
-	verl is supported on ROCm 6.2.0.
-
-.. _verl-recommendations:
-
-Use cases and recommendations
-================================================================================
-
-The benefits of verl in large-scale reinforcement learning from human feedback (RLHF) are discussed in the `Reinforcement Learning from Human Feedback on AMD GPUs with verl and ROCm Integration <https://rocm.blogs.amd.com/artificial-intelligence/verl-large-scale/README.html>`_ blog.
-
-.. _verl-supported_features:
-
-Supported features
-===============================================================================
-
-The following table shows verl on ROCm support for GPU-accelerated modules.
-
-.. list-table::
-    :header-rows: 1
-
-    * - Module
-      - Description
-      - verl version
-      - ROCm version
-    * - ``FSDP``
-      - Training engine
-      - 0.3.0.post0
-      - 6.2.0
-    * - ``vllm``
-      - Inference engine
-      - 0.3.0.post0
-      - 6.2.0
-
-.. _verl-docker-compat:
-
-Docker image compatibility
-================================================================================
-
-.. |docker-icon| raw:: html
-
-   <i class="fab fa-docker"></i>
-
-AMD validates and publishes ready-made `ROCm verl Docker images <https://hub.docker.com/r/rocm/verl/tags>`_
-with ROCm backends on Docker Hub. The following Docker image tags and associated inventories represent the available verl versions from the official Docker Hub. 
-
-.. list-table:: 
-    :header-rows: 1
-
-    *   - Docker image
-        - ROCm
-        - verl
-        - Ubuntu
-        - Pytorch
-        - Python
-        - vllm
-
-    *   - .. raw:: html
-
-            <a href="https://hub.docker.com/layers/rocm/verl/verl-0.3.0.post0_rocm6.2_vllm0.6.3/images/sha256-cbe423803fd7850448b22444176bee06f4dcf22cd3c94c27732752d3a39b04b2"><i class="fab fa-docker fa-lg"></i> rocm/verl</a>
-        - `6.2.0 <https://repo.radeon.com/rocm/apt/6.2/>`_
-        - `0.3.0post0 <https://github.com/volcengine/verl/releases/tag/v0.3.0.post0>`_
-        - 20.04
-        - `2.5.0 <https://github.com/ROCm/pytorch/tree/release/2.5>`_
-        - `3.9.19 <https://www.python.org/downloads/release/python-3919/>`_
-        - `0.6.3 <https://github.com/vllm-project/vllm/releases/tag/v0.6.3>`_
@@ -66,7 +66,6 @@ architecture.
 * [AMD Instinct MI50/Vega 7nm ISA](https://www.amd.com/system/files/TechDocs/vega-7nm-shader-instruction-set-architecture.pdf)
 * [AMD Instinct MI25/Vega ISA](https://www.amd.com/system/files/TechDocs/vega-shader-instruction-set-architecture.pdf)
 * [AMD GCN3 ISA](https://www.amd.com/system/files/TechDocs/gcn3-instruction-set-architecture.pdf)
-* [AMD Vega Architecture White Paper](https://en.wikichip.org/w/images/a/a1/vega-whitepaper.pdf)

 :::

@@ -34,7 +34,7 @@ Runtime

 ```{code-block} shell
 :caption: Example to expose the 1. device and a device based on UUID.
-export ROCR_VISIBLE_DEVICES="0,GPU-DEADBEEFDEADBEEF"
+export ROCR_VISIBLE_DEVICES="0,GPU-4b2c1a9f-8d3e-6f7a-b5c9-2e4d8a1f6c3b"
 ```

 ### `GPU_DEVICE_ORDINAL`
@@ -8,6 +8,7 @@ import os
 import shutil
 import sys
 from pathlib import Path
+from subprocess import run

 gh_release_path = os.path.join("..", "RELEASE.md")
 gh_changelog_path = os.path.join("..", "CHANGELOG.md")
@@ -80,37 +81,33 @@ latex_elements = {
 }

 html_baseurl = os.environ.get("READTHEDOCS_CANONICAL_URL", "rocm.docs.amd.com")
-html_context = {}
+html_context = {"docs_header_version": "7.1.1"}
 if os.environ.get("READTHEDOCS", "") == "True":
    html_context["READTHEDOCS"] = True

+# Check if the branch is a docs/ branch
+official_branch = run(["git", "rev-parse", "--abbrev-ref", "HEAD"], capture_output=True, text=True).stdout.find("docs/")
+
 # configurations for PDF output by Read the Docs
 project = "ROCm Documentation"
 project_path = os.path.abspath(".").replace("\\", "/")
 author = "Advanced Micro Devices, Inc."
 copyright = "Copyright (c) 2025 Advanced Micro Devices, Inc. All rights reserved."
-version = "7.0.2"
-release = "7.0.2"
+version = "7.2.0"
+release = "7.2.0"
 setting_all_article_info = True
 all_article_info_os = ["linux", "windows"]
 all_article_info_author = ""

 # pages with specific settings
 article_pages = [
-    {"file": "about/release-notes", "os": ["linux"], "date": "2025-10-10"},
+    {"file": "about/release-notes", "os": ["linux"], "date": "2026-01-21"},
    {"file": "release/changelog", "os": ["linux"],},
    {"file": "compatibility/compatibility-matrix", "os": ["linux"]},
    {"file": "compatibility/ml-compatibility/pytorch-compatibility", "os": ["linux"]},
    {"file": "compatibility/ml-compatibility/tensorflow-compatibility", "os": ["linux"]},
    {"file": "compatibility/ml-compatibility/jax-compatibility", "os": ["linux"]},
-    {"file": "compatibility/ml-compatibility/verl-compatibility", "os": ["linux"]},
-    {"file": "compatibility/ml-compatibility/stanford-megatron-lm-compatibility", "os": ["linux"]},
    {"file": "compatibility/ml-compatibility/dgl-compatibility", "os": ["linux"]},
-    {"file": "compatibility/ml-compatibility/megablocks-compatibility", "os": ["linux"]},
-    {"file": "compatibility/ml-compatibility/taichi-compatibility", "os": ["linux"]},
-    {"file": "compatibility/ml-compatibility/ray-compatibility", "os": ["linux"]},
-    {"file": "compatibility/ml-compatibility/llama-cpp-compatibility", "os": ["linux"]},
-    {"file": "compatibility/ml-compatibility/flashinfer-compatibility", "os": ["linux"]},
    {"file": "how-to/deep-learning-rocm", "os": ["linux"]},

    {"file": "how-to/rocm-for-ai/index", "os": ["linux"]},
@@ -132,9 +129,17 @@ article_pages = [
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.5", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.6", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.7", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.8", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.9", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.10", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.11", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-primus-migration-guide", "os": ["linux"]},
-    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-megatron-v25.7", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/primus-megatron", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-megatron-v25.7", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-megatron-v25.8", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-megatron-v25.9", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-megatron-v25.10", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-megatron-v25.11", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/pytorch-training", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-history", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.3", "os": ["linux"]},
@@ -142,12 +147,22 @@ article_pages = [
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.5", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.6", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.7", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.8", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.9", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.10", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.11", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/primus-pytorch", "os": ["linux"]},
-    {"file": "how-to/rocm-for-ai/training/benchmark-docker/pytorch-training", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-pytorch-v25.8", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-pytorch-v25.9", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-pytorch-v25.10", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-pytorch-v25.11", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/jax-maxtext", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-history", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-v25.4", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-v25.5", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-v25.9", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-v25.11", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-v26.1", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/mpt-llm-foundry", "os": ["linux"]},

    {"file": "how-to/rocm-for-ai/fine-tuning/index", "os": ["linux"]},
@@ -173,8 +188,23 @@ article_pages = [
    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.9.1-20250702", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.9.1-20250715", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.10.0-20250812", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.10.1-20250909", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.10.2-20251006", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.11.1-20251103", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/sglang-history", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/pytorch-inference", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/sglang", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/vllm-mori-distributed", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/sglang-mori-distributed", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/sglang-distributed", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/inference/xdit-diffusion-inference", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-25.10", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-25.11", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-25.12", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-25.13", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-26.1", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-26.2", "os": ["linux"]},
+
    {"file": "how-to/rocm-for-ai/inference/deploy-your-model", "os": ["linux"]},

    {"file": "how-to/rocm-for-ai/inference-optimization/index", "os": ["linux"]},
@@ -202,7 +232,7 @@ external_toc_path = "./sphinx/_toc.yml"
 # Add the _extensions directory to Python's search path
 sys.path.append(str(Path(__file__).parent / 'extension'))

-extensions = ["rocm_docs", "sphinx_reredirects", "sphinx_sitemap", "sphinxcontrib.datatemplates", "version-ref", "csv-to-list-table"]
+extensions = ["rocm_docs", "sphinx_reredirects", "sphinx_sitemap", "sphinxcontrib.datatemplates", "remote-content", "version-ref", "csv-to-list-table"]

 compatibility_matrix_file = str(Path(__file__).parent / 'compatibility/compatibility-matrix-historical-6.0.csv')

@@ -212,10 +242,14 @@ external_projects_current_project = "rocm"
 # external_projects_remote_repository = ""

 html_baseurl = os.environ.get("READTHEDOCS_CANONICAL_URL", "https://rocm-stg.amd.com/")
-html_context = {}
+html_context = {"docs_header_version": "7.1.0"}
 if os.environ.get("READTHEDOCS", "") == "True":
    html_context["READTHEDOCS"] = True

+html_context["official_branch"] = official_branch
+html_context["version"] = version
+html_context["release"] = release
+
 html_theme = "rocm_docs_theme"
 html_theme_options = {"flavor": "rocm-docs-home"}

@@ -241,3 +275,6 @@ html_context = {
    "granularity_type" : [('Coarse-grained', 'coarse-grained'), ('Fine-grained', 'fine-grained')],
    "scope_type" : [('Device', 'device'), ('System', 'system')]
 }
+
+# Disable figure and table numbering
+numfig = False
@@ -0,0 +1,316 @@
+dockers:
+  - pull_tag: rocm/vllm:rocm7.0.0_vllm_0.10.2_20251006
+    docker_hub_url: https://hub.docker.com/layers/rocm/vllm/rocm7.0.0_vllm_0.10.2_20251006/images/sha256-94fd001964e1cf55c3224a445b1fb5be31a7dac302315255db8422d813edd7f5
+    components:
+      ROCm: 7.0.0
+      vLLM: 0.10.2 (0.11.0rc2.dev160+g790d22168.rocm700)
+      PyTorch: 2.9.0a0+git1c57644
+      hipBLASLt: 1.0.0
+    dockerfile:
+      commit: 790d22168820507f3105fef29596549378cfe399
+model_groups:
+  - group: Meta Llama
+    tag: llama
+    models:
+      - model: Llama 2 70B
+        mad_tag: pyt_vllm_llama-2-70b
+        model_repo: meta-llama/Llama-2-70b-chat-hf
+        url: https://huggingface.co/meta-llama/Llama-2-70b-chat-hf
+        precision: float16
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 4096
+          max_model_len: 4096
+      - model: Llama 3.1 8B
+        mad_tag: pyt_vllm_llama-3.1-8b
+        model_repo: meta-llama/Llama-3.1-8B-Instruct
+        url: https://huggingface.co/meta-llama/Llama-3.1-8B
+        precision: float16
+        config:
+          tp: 1
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 131072
+          max_model_len: 8192
+      - model: Llama 3.1 8B FP8
+        mad_tag: pyt_vllm_llama-3.1-8b_fp8
+        model_repo: amd/Llama-3.1-8B-Instruct-FP8-KV
+        url: https://huggingface.co/amd/Llama-3.1-8B-Instruct-FP8-KV
+        precision: float8
+        config:
+          tp: 1
+          dtype: auto
+          kv_cache_dtype: fp8
+          max_num_batched_tokens: 131072
+          max_model_len: 8192
+      - model: Llama 3.1 405B
+        mad_tag: pyt_vllm_llama-3.1-405b
+        model_repo: meta-llama/Llama-3.1-405B-Instruct
+        url: https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct
+        precision: float16
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 131072
+          max_model_len: 8192
+      - model: Llama 3.1 405B FP8
+        mad_tag: pyt_vllm_llama-3.1-405b_fp8
+        model_repo: amd/Llama-3.1-405B-Instruct-FP8-KV
+        url: https://huggingface.co/amd/Llama-3.1-405B-Instruct-FP8-KV
+        precision: float8
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: fp8
+          max_num_batched_tokens: 131072
+          max_model_len: 8192
+      - model: Llama 3.1 405B MXFP4
+        mad_tag: pyt_vllm_llama-3.1-405b_fp4
+        model_repo: amd/Llama-3.1-405B-Instruct-MXFP4-Preview
+        url: https://huggingface.co/amd/Llama-3.1-405B-Instruct-MXFP4-Preview
+        precision: float4
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: fp8
+          max_num_batched_tokens: 131072
+          max_model_len: 8192
+      - model: Llama 3.3 70B
+        mad_tag: pyt_vllm_llama-3.3-70b
+        model_repo: meta-llama/Llama-3.3-70B-Instruct
+        url: https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct
+        precision: float16
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 131072
+          max_model_len: 8192
+      - model: Llama 3.3 70B FP8
+        mad_tag: pyt_vllm_llama-3.3-70b_fp8
+        model_repo: amd/Llama-3.3-70B-Instruct-FP8-KV
+        url: https://huggingface.co/amd/Llama-3.3-70B-Instruct-FP8-KV
+        precision: float8
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: fp8
+          max_num_batched_tokens: 131072
+          max_model_len: 8192
+      - model: Llama 3.3 70B MXFP4
+        mad_tag: pyt_vllm_llama-3.3-70b_fp4
+        model_repo: amd/Llama-3.3-70B-Instruct-MXFP4-Preview
+        url: https://huggingface.co/amd/Llama-3.3-70B-Instruct-MXFP4-Preview
+        precision: float4
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: fp8
+          max_num_batched_tokens: 131072
+          max_model_len: 8192
+      - model: Llama 4 Scout 17Bx16E
+        mad_tag: pyt_vllm_llama-4-scout-17b-16e
+        model_repo: meta-llama/Llama-4-Scout-17B-16E-Instruct
+        url: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct
+        precision: float16
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 32768
+          max_model_len: 8192
+      - model: Llama 4 Maverick 17Bx128E
+        mad_tag: pyt_vllm_llama-4-maverick-17b-128e
+        model_repo: meta-llama/Llama-4-Maverick-17B-128E-Instruct
+        url: https://huggingface.co/meta-llama/Llama-4-Maverick-17B-128E-Instruct
+        precision: float16
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 32768
+          max_model_len: 8192
+      - model: Llama 4 Maverick 17Bx128E FP8
+        mad_tag: pyt_vllm_llama-4-maverick-17b-128e_fp8
+        model_repo: meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8
+        url: https://huggingface.co/meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8
+        precision: float8
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: fp8
+          max_num_batched_tokens: 131072
+          max_model_len: 8192
+  - group: DeepSeek
+    tag: deepseek
+    models:
+      - model: DeepSeek R1 0528 FP8
+        mad_tag: pyt_vllm_deepseek-r1
+        model_repo: deepseek-ai/DeepSeek-R1-0528
+        url: https://huggingface.co/deepseek-ai/DeepSeek-R1-0528
+        precision: float8
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: fp8
+          max_num_seqs: 1024
+          max_num_batched_tokens: 131072
+          max_model_len: 8192
+  - group: OpenAI GPT OSS
+    tag: gpt-oss
+    models:
+      - model: GPT OSS 20B
+        mad_tag: pyt_vllm_gpt-oss-20b
+        model_repo: openai/gpt-oss-20b
+        url: https://huggingface.co/openai/gpt-oss-20b
+        precision: bfloat16
+        config:
+          tp: 1
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 8192
+          max_model_len: 8192
+      - model: GPT OSS 120B
+        mad_tag: pyt_vllm_gpt-oss-120b
+        model_repo: openai/gpt-oss-120b
+        url: https://huggingface.co/openai/gpt-oss-120b
+        precision: bfloat16
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 8192
+          max_model_len: 8192
+  - group: Mistral AI
+    tag: mistral
+    models:
+      - model: Mixtral MoE 8x7B
+        mad_tag: pyt_vllm_mixtral-8x7b
+        model_repo: mistralai/Mixtral-8x7B-Instruct-v0.1
+        url: https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1
+        precision: float16
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 32768
+          max_model_len: 8192
+      - model: Mixtral MoE 8x7B FP8
+        mad_tag: pyt_vllm_mixtral-8x7b_fp8
+        model_repo: amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV
+        url: https://huggingface.co/amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV
+        precision: float8
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: fp8
+          max_num_batched_tokens: 32768
+          max_model_len: 8192
+      - model: Mixtral MoE 8x22B
+        mad_tag: pyt_vllm_mixtral-8x22b
+        model_repo: mistralai/Mixtral-8x22B-Instruct-v0.1
+        url: https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1
+        precision: float16
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 65536
+          max_model_len: 8192
+      - model: Mixtral MoE 8x22B FP8
+        mad_tag: pyt_vllm_mixtral-8x22b_fp8
+        model_repo: amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
+        url: https://huggingface.co/amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
+        precision: float8
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: fp8
+          max_num_batched_tokens: 65536
+          max_model_len: 8192
+  - group: Qwen
+    tag: qwen
+    models:
+      - model: Qwen3 8B
+        mad_tag: pyt_vllm_qwen3-8b
+        model_repo: Qwen/Qwen3-8B
+        url: https://huggingface.co/Qwen/Qwen3-8B
+        precision: float16
+        config:
+          tp: 1
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 40960
+          max_model_len: 8192
+      - model: Qwen3 32B
+        mad_tag: pyt_vllm_qwen3-32b
+        model_repo: Qwen/Qwen3-32b
+        url: https://huggingface.co/Qwen/Qwen3-32B
+        precision: float16
+        config:
+          tp: 1
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 40960
+          max_model_len: 8192
+      - model: Qwen3 30B A3B
+        mad_tag: pyt_vllm_qwen3-30b-a3b
+        model_repo: Qwen/Qwen3-30B-A3B
+        url: https://huggingface.co/Qwen/Qwen3-30B-A3B
+        precision: float16
+        config:
+          tp: 1
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 40960
+          max_model_len: 8192
+      - model: Qwen3 30B A3B FP8
+        mad_tag: pyt_vllm_qwen3-30b-a3b_fp8
+        model_repo: Qwen/Qwen3-30B-A3B-FP8
+        url: https://huggingface.co/Qwen/Qwen3-30B-A3B-FP8
+        precision: float16
+        config:
+          tp: 1
+          dtype: auto
+          kv_cache_dtype: fp8
+          max_num_batched_tokens: 40960
+          max_model_len: 8192
+      - model: Qwen3 235B A22B
+        mad_tag: pyt_vllm_qwen3-235b-a22b
+        model_repo: Qwen/Qwen3-235B-A22B
+        url: https://huggingface.co/Qwen/Qwen3-235B-A22B
+        precision: float16
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 40960
+          max_model_len: 8192
+      - model: Qwen3 235B A22B FP8
+        mad_tag: pyt_vllm_qwen3-235b-a22b_fp8
+        model_repo: Qwen/Qwen3-235B-A22B-FP8
+        url: https://huggingface.co/Qwen/Qwen3-235B-A22B-FP8
+        precision: float8
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: fp8
+          max_num_batched_tokens: 40960
+          max_model_len: 8192
+  - group: Microsoft Phi
+    tag: phi
+    models:
+      - model: Phi-4
+        mad_tag: pyt_vllm_phi-4
+        model_repo: microsoft/phi-4
+        url: https://huggingface.co/microsoft/phi-4
+        precision: float16
+        config:
+          tp: 1
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 16384
+          max_model_len: 8192
@@ -0,0 +1,316 @@
+dockers:
+  - pull_tag: rocm/vllm:rocm7.0.0_vllm_0.11.1_20251103
+    docker_hub_url: https://hub.docker.com/layers/rocm/vllm/rocm7.0.0_vllm_0.11.1_20251103/images/sha256-8d60429043d4d00958da46039a1de0d9b82df814d45da482497eef26a6076506
+    components:
+      ROCm: 7.0.0
+      vLLM: 0.11.1 (0.11.1rc2.dev141+g38f225c2a.rocm700)
+      PyTorch: 2.9.0a0+git1c57644
+      hipBLASLt: 1.0.0
+    dockerfile:
+      commit: 38f225c2abeadc04c2cc398814c2f53ea02c3c72
+model_groups:
+  - group: Meta Llama
+    tag: llama
+    models:
+      - model: Llama 2 70B
+        mad_tag: pyt_vllm_llama-2-70b
+        model_repo: meta-llama/Llama-2-70b-chat-hf
+        url: https://huggingface.co/meta-llama/Llama-2-70b-chat-hf
+        precision: float16
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 4096
+          max_model_len: 4096
+      - model: Llama 3.1 8B
+        mad_tag: pyt_vllm_llama-3.1-8b
+        model_repo: meta-llama/Llama-3.1-8B-Instruct
+        url: https://huggingface.co/meta-llama/Llama-3.1-8B
+        precision: float16
+        config:
+          tp: 1
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 131072
+          max_model_len: 8192
+      - model: Llama 3.1 8B FP8
+        mad_tag: pyt_vllm_llama-3.1-8b_fp8
+        model_repo: amd/Llama-3.1-8B-Instruct-FP8-KV
+        url: https://huggingface.co/amd/Llama-3.1-8B-Instruct-FP8-KV
+        precision: float8
+        config:
+          tp: 1
+          dtype: auto
+          kv_cache_dtype: fp8
+          max_num_batched_tokens: 131072
+          max_model_len: 8192
+      - model: Llama 3.1 405B
+        mad_tag: pyt_vllm_llama-3.1-405b
+        model_repo: meta-llama/Llama-3.1-405B-Instruct
+        url: https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct
+        precision: float16
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 131072
+          max_model_len: 8192
+      - model: Llama 3.1 405B FP8
+        mad_tag: pyt_vllm_llama-3.1-405b_fp8
+        model_repo: amd/Llama-3.1-405B-Instruct-FP8-KV
+        url: https://huggingface.co/amd/Llama-3.1-405B-Instruct-FP8-KV
+        precision: float8
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: fp8
+          max_num_batched_tokens: 131072
+          max_model_len: 8192
+      - model: Llama 3.1 405B MXFP4
+        mad_tag: pyt_vllm_llama-3.1-405b_fp4
+        model_repo: amd/Llama-3.1-405B-Instruct-MXFP4-Preview
+        url: https://huggingface.co/amd/Llama-3.1-405B-Instruct-MXFP4-Preview
+        precision: float4
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: fp8
+          max_num_batched_tokens: 131072
+          max_model_len: 8192
+      - model: Llama 3.3 70B
+        mad_tag: pyt_vllm_llama-3.3-70b
+        model_repo: meta-llama/Llama-3.3-70B-Instruct
+        url: https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct
+        precision: float16
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 131072
+          max_model_len: 8192
+      - model: Llama 3.3 70B FP8
+        mad_tag: pyt_vllm_llama-3.3-70b_fp8
+        model_repo: amd/Llama-3.3-70B-Instruct-FP8-KV
+        url: https://huggingface.co/amd/Llama-3.3-70B-Instruct-FP8-KV
+        precision: float8
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: fp8
+          max_num_batched_tokens: 131072
+          max_model_len: 8192
+      - model: Llama 3.3 70B MXFP4
+        mad_tag: pyt_vllm_llama-3.3-70b_fp4
+        model_repo: amd/Llama-3.3-70B-Instruct-MXFP4-Preview
+        url: https://huggingface.co/amd/Llama-3.3-70B-Instruct-MXFP4-Preview
+        precision: float4
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: fp8
+          max_num_batched_tokens: 131072
+          max_model_len: 8192
+      - model: Llama 4 Scout 17Bx16E
+        mad_tag: pyt_vllm_llama-4-scout-17b-16e
+        model_repo: meta-llama/Llama-4-Scout-17B-16E-Instruct
+        url: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct
+        precision: float16
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 32768
+          max_model_len: 8192
+      - model: Llama 4 Maverick 17Bx128E
+        mad_tag: pyt_vllm_llama-4-maverick-17b-128e
+        model_repo: meta-llama/Llama-4-Maverick-17B-128E-Instruct
+        url: https://huggingface.co/meta-llama/Llama-4-Maverick-17B-128E-Instruct
+        precision: float16
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 32768
+          max_model_len: 8192
+      - model: Llama 4 Maverick 17Bx128E FP8
+        mad_tag: pyt_vllm_llama-4-maverick-17b-128e_fp8
+        model_repo: meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8
+        url: https://huggingface.co/meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8
+        precision: float8
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: fp8
+          max_num_batched_tokens: 131072
+          max_model_len: 8192
+  - group: DeepSeek
+    tag: deepseek
+    models:
+      - model: DeepSeek R1 0528 FP8
+        mad_tag: pyt_vllm_deepseek-r1
+        model_repo: deepseek-ai/DeepSeek-R1-0528
+        url: https://huggingface.co/deepseek-ai/DeepSeek-R1-0528
+        precision: float8
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: fp8
+          max_num_seqs: 1024
+          max_num_batched_tokens: 131072
+          max_model_len: 8192
+  - group: OpenAI GPT OSS
+    tag: gpt-oss
+    models:
+      - model: GPT OSS 20B
+        mad_tag: pyt_vllm_gpt-oss-20b
+        model_repo: openai/gpt-oss-20b
+        url: https://huggingface.co/openai/gpt-oss-20b
+        precision: bfloat16
+        config:
+          tp: 1
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 8192
+          max_model_len: 8192
+      - model: GPT OSS 120B
+        mad_tag: pyt_vllm_gpt-oss-120b
+        model_repo: openai/gpt-oss-120b
+        url: https://huggingface.co/openai/gpt-oss-120b
+        precision: bfloat16
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 8192
+          max_model_len: 8192
+  - group: Mistral AI
+    tag: mistral
+    models:
+      - model: Mixtral MoE 8x7B
+        mad_tag: pyt_vllm_mixtral-8x7b
+        model_repo: mistralai/Mixtral-8x7B-Instruct-v0.1
+        url: https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1
+        precision: float16
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 32768
+          max_model_len: 8192
+      - model: Mixtral MoE 8x7B FP8
+        mad_tag: pyt_vllm_mixtral-8x7b_fp8
+        model_repo: amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV
+        url: https://huggingface.co/amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV
+        precision: float8
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: fp8
+          max_num_batched_tokens: 32768
+          max_model_len: 8192
+      - model: Mixtral MoE 8x22B
+        mad_tag: pyt_vllm_mixtral-8x22b
+        model_repo: mistralai/Mixtral-8x22B-Instruct-v0.1
+        url: https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1
+        precision: float16
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 65536
+          max_model_len: 8192
+      - model: Mixtral MoE 8x22B FP8
+        mad_tag: pyt_vllm_mixtral-8x22b_fp8
+        model_repo: amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
+        url: https://huggingface.co/amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
+        precision: float8
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: fp8
+          max_num_batched_tokens: 65536
+          max_model_len: 8192
+  - group: Qwen
+    tag: qwen
+    models:
+      - model: Qwen3 8B
+        mad_tag: pyt_vllm_qwen3-8b
+        model_repo: Qwen/Qwen3-8B
+        url: https://huggingface.co/Qwen/Qwen3-8B
+        precision: float16
+        config:
+          tp: 1
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 40960
+          max_model_len: 8192
+      - model: Qwen3 32B
+        mad_tag: pyt_vllm_qwen3-32b
+        model_repo: Qwen/Qwen3-32b
+        url: https://huggingface.co/Qwen/Qwen3-32B
+        precision: float16
+        config:
+          tp: 1
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 40960
+          max_model_len: 8192
+      - model: Qwen3 30B A3B
+        mad_tag: pyt_vllm_qwen3-30b-a3b
+        model_repo: Qwen/Qwen3-30B-A3B
+        url: https://huggingface.co/Qwen/Qwen3-30B-A3B
+        precision: float16
+        config:
+          tp: 1
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 40960
+          max_model_len: 8192
+      - model: Qwen3 30B A3B FP8
+        mad_tag: pyt_vllm_qwen3-30b-a3b_fp8
+        model_repo: Qwen/Qwen3-30B-A3B-FP8
+        url: https://huggingface.co/Qwen/Qwen3-30B-A3B-FP8
+        precision: float16
+        config:
+          tp: 1
+          dtype: auto
+          kv_cache_dtype: fp8
+          max_num_batched_tokens: 40960
+          max_model_len: 8192
+      - model: Qwen3 235B A22B
+        mad_tag: pyt_vllm_qwen3-235b-a22b
+        model_repo: Qwen/Qwen3-235B-A22B
+        url: https://huggingface.co/Qwen/Qwen3-235B-A22B
+        precision: float16
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 40960
+          max_model_len: 8192
+      - model: Qwen3 235B A22B FP8
+        mad_tag: pyt_vllm_qwen3-235b-a22b_fp8
+        model_repo: Qwen/Qwen3-235B-A22B-FP8
+        url: https://huggingface.co/Qwen/Qwen3-235B-A22B-FP8
+        precision: float8
+        config:
+          tp: 8
+          dtype: auto
+          kv_cache_dtype: fp8
+          max_num_batched_tokens: 40960
+          max_model_len: 8192
+  - group: Microsoft Phi
+    tag: phi
+    models:
+      - model: Phi-4
+        mad_tag: pyt_vllm_phi-4
+        model_repo: microsoft/phi-4
+        url: https://huggingface.co/microsoft/phi-4
+        precision: float16
+        config:
+          tp: 1
+          dtype: auto
+          kv_cache_dtype: auto
+          max_num_batched_tokens: 16384
+          max_model_len: 8192
@@ -0,0 +1,55 @@
+xdit_diffusion_inference:
+  docker:
+    pull_tag: rocm/pytorch-xdit:v25.10
+    docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-xdit/v25.10/images/sha256-d79715ff18a9470e3f907cec8a9654d6b783c63370b091446acffc0de4d7070e
+    ROCm: 7.9.0
+    components:
+      TheRock: 7afbe45
+      rccl: 9b04b2a
+      composable_kernel: b7a806f
+      rocm-libraries: f104555
+      rocm-systems: 25922d0
+      torch: 2.10.0a0+gite9c9017
+      torchvision: 0.22.0a0+966da7e
+      triton: 3.5.0+git52e49c12
+      accelerate: 1.11.0.dev0
+      aiter: 0.1.5.post4.dev20+ga25e55e79
+      diffusers: 0.36.0.dev0
+      xfuser: 0.4.4
+      yunchang: 0.6.3.post1
+
+  model_groups:
+    - group: Hunyuan Video
+      tag: hunyuan
+      models:
+        - model: Hunyuan Video
+          model_name: hunyuanvideo
+          model_repo: tencent/HunyuanVideo
+          revision: refs/pr/18
+          url: https://huggingface.co/tencent/HunyuanVideo
+          github: https://github.com/Tencent-Hunyuan/HunyuanVideo
+          mad_tag: pyt_xdit_hunyuanvideo
+    - group: Wan-AI
+      tag: wan
+      models:
+        - model: Wan2.1
+          model_name: wan2_1-i2v-14b-720p
+          model_repo: Wan-AI/Wan2.1-I2V-14B-720P
+          url: https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P
+          github: https://github.com/Wan-Video/Wan2.1
+          mad_tag: pyt_xdit_wan_2_1
+        - model: Wan2.2
+          model_name: wan2_2-i2v-a14b
+          model_repo: Wan-AI/Wan2.2-I2V-A14B
+          url: https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B
+          github: https://github.com/Wan-Video/Wan2.2
+          mad_tag: pyt_xdit_wan_2_2
+    - group: FLUX
+      tag: flux
+      models:
+        - model: FLUX.1
+          model_name: FLUX.1-dev
+          model_repo: black-forest-labs/FLUX.1-dev
+          url: https://huggingface.co/black-forest-labs/FLUX.1-dev
+          github: https://github.com/black-forest-labs/flux
+          mad_tag: pyt_xdit_flux
@@ -0,0 +1,109 @@
+xdit_diffusion_inference:
+  docker:
+    - version: v25-11
+      pull_tag: rocm/pytorch-xdit:v25.11
+      docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-xdit/v25.11/images/sha256-c9fa659439bb024f854b4d5eea598347251b02c341c55f66c98110832bde4216
+      ROCm: 7.10.0
+      supported_models:
+        - group: Hunyuan Video
+          models:
+            - Hunyuan Video
+        - group: Wan-AI
+          models:
+            - Wan2.1
+            - Wan2.2
+        - group: FLUX
+          models:
+            - FLUX.1
+      whats_new:
+        - "Minor bug fixes and clarifications to READMEs."
+        - "Bumps TheRock, AITER, Diffusers, xDiT versions."
+        - "Changes Aiter rounding mode for faster gfx942 FWD Attention."
+      components:
+        TheRock: 3e3f834
+        rccl: d23d18f
+        composable_kernel: 2570462
+        rocm-libraries: 0588f07
+        rocm-systems: 473025a
+        torch: 73adac
+        torchvision: f5c6c2e
+        triton: 7416ffc
+        accelerate: 34c1779
+        aiter: de14bec
+        diffusers: 40528e9
+        xfuser: 83978b5
+        yunchang: 2c9b712
+
+    - version: v25-10
+      pull_tag: rocm/pytorch-xdit:v25.10
+      docker_hub_url: https://hub.docker.com/r/rocm/pytorch-xdit
+      ROCm: 7.9.0
+      supported_models:
+        - group: Hunyuan Video
+          models:
+            - Hunyuan Video
+        - group: Wan-AI
+          models:
+            - Wan2.1
+            - Wan2.2
+        - group: FLUX
+          models:
+            - FLUX.1
+      whats_new:
+        - "First official xDiT Docker Release for Diffusion Inference."
+        - "Supports gfx942 and gfx950 series (AMD Instinct™ MI300X, MI325X, MI350X, and MI355X)."
+        - "Support Wan 2.1, Wan 2.2, HunyuanVideo and Flux workloads."
+      components:
+        TheRock: 7afbe45
+        rccl: 9b04b2a
+        composable_kernel: b7a806f
+        rocm-libraries: f104555
+        rocm-systems: 25922d0
+        torch: 2.10.0a0+gite9c9017
+        torchvision: 0.22.0a0+966da7e
+        triton: 3.5.0+git52e49c12
+        accelerate: 1.11.0.dev0
+        aiter: 0.1.5.post4.dev20+ga25e55e79
+        diffusers: 0.36.0.dev0
+        xfuser: 0.4.4
+        yunchang: 0.6.3.post1
+
+  model_groups:
+    - group: Hunyuan Video
+      tag: hunyuan
+      models:
+        - model: Hunyuan Video
+          page_tag: hunyuan_tag
+          model_name: hunyuanvideo
+          model_repo: tencent/HunyuanVideo
+          revision: refs/pr/18
+          url: https://huggingface.co/tencent/HunyuanVideo
+          github: https://github.com/Tencent-Hunyuan/HunyuanVideo
+          mad_tag: pyt_xdit_hunyuanvideo
+    - group: Wan-AI
+      tag: wan
+      models:
+        - model: Wan2.1
+          page_tag: wan_21_tag
+          model_name: wan2_1-i2v-14b-720p
+          model_repo: Wan-AI/Wan2.1-I2V-14B-720P
+          url: https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P
+          github: https://github.com/Wan-Video/Wan2.1
+          mad_tag: pyt_xdit_wan_2_1
+        - model: Wan2.2
+          page_tag: wan_22_tag
+          model_name: wan2_2-i2v-a14b
+          model_repo: Wan-AI/Wan2.2-I2V-A14B
+          url: https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B
+          github: https://github.com/Wan-Video/Wan2.2
+          mad_tag: pyt_xdit_wan_2_2
+    - group: FLUX
+      tag: flux
+      models:
+        - model: FLUX.1
+          page_tag: flux_1_tag
+          model_name: FLUX.1-dev
+          model_repo: black-forest-labs/FLUX.1-dev
+          url: https://huggingface.co/black-forest-labs/FLUX.1-dev
+          github: https://github.com/black-forest-labs/flux
+          mad_tag: pyt_xdit_flux
@@ -0,0 +1,91 @@
+docker:
+  pull_tag: rocm/pytorch-xdit:v25.12
+  docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-xdit/v25.12/images/sha256-e06895132316bf3c393366b70a91eaab6755902dad0100e6e2b38310547d9256
+  ROCm: 7.10.0
+  whats_new:
+      - "Adds T2V and TI2V support for Wan models."
+      - "Adds support for SD-3.5 T2I model."
+  components:
+    TheRock: 
+      version: 3e3f834
+      url: https://github.com/ROCm/TheRock
+    rccl:
+      version: d23d18f
+      url: https://github.com/ROCm/rccl
+    composable_kernel:
+      version: 2570462
+      url: https://github.com/ROCm/composable_kernel
+    rocm-libraries:
+      version: 0588f07
+      url: https://github.com/ROCm/rocm-libraries
+    rocm-systems:
+      version: 473025a
+      url: https://github.com/ROCm/rocm-systems
+    torch:
+      version: 73adac
+      url: https://github.com/pytorch/pytorch
+    torchvision:
+      version: f5c6c2e
+      url: https://github.com/pytorch/vision
+    triton:
+      version: 7416ffc
+      url: https://github.com/triton-lang/triton
+    accelerate:
+      version: 34c1779
+      url: https://github.com/huggingface/accelerate
+    aiter:
+      version: de14bec
+      url: https://github.com/ROCm/aiter
+    diffusers:
+      version: 40528e9
+      url: https://github.com/huggingface/diffusers
+    xfuser:
+      version: ccba9d5
+      url: https://github.com/xdit-project/xDiT
+    yunchang:
+      version: 2c9b712
+      url: https://github.com/feifeibear/long-context-attention
+  supported_models:
+    - group: Hunyuan Video
+      js_tag: hunyuan
+      models:
+        - model: Hunyuan Video
+          model_repo: tencent/HunyuanVideo
+          revision: refs/pr/18
+          url: https://huggingface.co/tencent/HunyuanVideo
+          github: https://github.com/Tencent-Hunyuan/HunyuanVideo
+          mad_tag: pyt_xdit_hunyuanvideo
+          js_tag: hunyuan_tag
+    - group: Wan-AI
+      js_tag: wan
+      models:
+        - model: Wan2.1
+          model_repo: Wan-AI/Wan2.1-I2V-14B-720P-Diffusers
+          url: https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P-Diffusers
+          github: https://github.com/Wan-Video/Wan2.1
+          mad_tag: pyt_xdit_wan_2_1
+          js_tag: wan_21_tag
+        - model: Wan2.2
+          model_repo: Wan-AI/Wan2.2-I2V-A14B-Diffusers
+          url: https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B-Diffusers
+          github: https://github.com/Wan-Video/Wan2.2
+          mad_tag: pyt_xdit_wan_2_2
+          js_tag: wan_22_tag
+    - group: FLUX
+      js_tag: flux
+      models:
+        - model: FLUX.1
+          model_repo: black-forest-labs/FLUX.1-dev
+          url: https://huggingface.co/black-forest-labs/FLUX.1-dev
+          github: https://github.com/black-forest-labs/flux
+          mad_tag: pyt_xdit_flux
+          js_tag: flux_1_tag
+    - group: Stable Diffusion
+      js_tag: stablediffusion
+      models:
+        - model: stable-diffusion-3.5-large
+          model_repo: stabilityai/stable-diffusion-3.5-large
+          url: https://huggingface.co/stabilityai/stable-diffusion-3.5-large
+          github: https://github.com/Stability-AI/sd3.5
+          mad_tag: pyt_xdit_sd_3_5
+          js_tag: stable_diffusion_3_5_large_tag
@@ -0,0 +1,105 @@
+docker:
+  pull_tag: rocm/pytorch-xdit:v25.13
+  docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-xdit/v25.13/images/sha256-81954713070d67bde08595e03f62110c8a3dd66a9ae17a77d611e01f83f0f4ef
+  ROCm: 7.11.0
+  whats_new:
+    - "Flux.1 Kontext support"
+    - "Flux.2 Dev support"
+    - "Flux FP8 GEMM support"
+    - "Hybrid FP8 attention support for Wan models"
+  components:
+    TheRock: 
+      version: 1728a81
+      url: https://github.com/ROCm/TheRock
+    rccl:
+      version: d23d18f
+      url: https://github.com/ROCm/rccl
+    composable_kernel:
+      version: ab0101c
+      url: https://github.com/ROCm/composable_kernel
+    rocm-libraries:
+      version: a2f7c35
+      url: https://github.com/ROCm/rocm-libraries
+    rocm-systems:
+      version: 659737c
+      url: https://github.com/ROCm/rocm-systems
+    torch:
+      version: 91be249
+      url: https://github.com/ROCm/pytorch
+    torchvision:
+      version: b919bd0
+      url: https://github.com/pytorch/vision
+    triton:
+      version: a272dfa
+      url: https://github.com/ROCm/triton
+    accelerate:
+      version: b521400f
+      url: https://github.com/huggingface/accelerate
+    aiter:
+      version: de14bec0
+      url: https://github.com/ROCm/aiter
+    diffusers:
+      version: a1f36ee3e
+      url: https://github.com/huggingface/diffusers
+    xfuser:
+      version: adf2681
+      url: https://github.com/xdit-project/xDiT
+    yunchang:
+      version: 2c9b712
+      url: https://github.com/feifeibear/long-context-attention
+  supported_models:
+    - group: Hunyuan Video
+      js_tag: hunyuan
+      models:
+        - model: Hunyuan Video
+          model_repo: tencent/HunyuanVideo
+          revision: refs/pr/18
+          url: https://huggingface.co/tencent/HunyuanVideo
+          github: https://github.com/Tencent-Hunyuan/HunyuanVideo
+          mad_tag: pyt_xdit_hunyuanvideo
+          js_tag: hunyuan_tag
+    - group: Wan-AI
+      js_tag: wan
+      models:
+        - model: Wan2.1
+          model_repo: Wan-AI/Wan2.1-I2V-14B-720P-Diffusers
+          url: https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P-Diffusers
+          github: https://github.com/Wan-Video/Wan2.1
+          mad_tag: pyt_xdit_wan_2_1
+          js_tag: wan_21_tag
+        - model: Wan2.2
+          model_repo: Wan-AI/Wan2.2-I2V-A14B-Diffusers
+          url: https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B-Diffusers
+          github: https://github.com/Wan-Video/Wan2.2
+          mad_tag: pyt_xdit_wan_2_2
+          js_tag: wan_22_tag
+    - group: FLUX
+      js_tag: flux
+      models:
+        - model: FLUX.1
+          model_repo: black-forest-labs/FLUX.1-dev
+          url: https://huggingface.co/black-forest-labs/FLUX.1-dev
+          github: https://github.com/black-forest-labs/flux
+          mad_tag: pyt_xdit_flux
+          js_tag: flux_1_tag
+        - model: FLUX.1 Kontext
+          model_repo: black-forest-labs/FLUX.1-Kontext-dev
+          url: https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev
+          github: https://github.com/black-forest-labs/flux
+          mad_tag: pyt_xdit_flux_kontext
+          js_tag: flux_1_kontext_tag
+        - model: FLUX.2
+          model_repo: black-forest-labs/FLUX.2-dev
+          url: https://huggingface.co/black-forest-labs/FLUX.2-dev
+          github: https://github.com/black-forest-labs/flux2
+          mad_tag: pyt_xdit_flux_2
+          js_tag: flux_2_tag
+    - group: StableDiffusion
+      js_tag: stablediffusion
+      models:
+        - model: stable-diffusion-3.5-large
+          model_repo: stabilityai/stable-diffusion-3.5-large
+          url: https://huggingface.co/stabilityai/stable-diffusion-3.5-large
+          github: https://github.com/Stability-AI/sd3.5
+          mad_tag: pyt_xdit_sd_3_5
+          js_tag: stable_diffusion_3_5_large_tag
@@ -0,0 +1,105 @@
+docker:
+  pull_tag: rocm/pytorch-xdit:v25.13
+  docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-xdit/v25.13/images/sha256-81954713070d67bde08595e03f62110c8a3dd66a9ae17a77d611e01f83f0f4ef
+  ROCm: 7.11.0
+  whats_new:
+    - "Flux.1 Kontext support"
+    - "Flux.2 Dev support"
+    - "Flux FP8 GEMM support"
+    - "Hybrid FP8 attention support for Wan models"
+  components:
+    TheRock: 
+      version: 1728a81
+      url: https://github.com/ROCm/TheRock
+    rccl:
+      version: d23d18f
+      url: https://github.com/ROCm/rccl
+    composable_kernel:
+      version: ab0101c
+      url: https://github.com/ROCm/composable_kernel
+    rocm-libraries:
+      version: a2f7c35
+      url: https://github.com/ROCm/rocm-libraries
+    rocm-systems:
+      version: 659737c
+      url: https://github.com/ROCm/rocm-systems
+    torch:
+      version: 91be249
+      url: https://github.com/ROCm/pytorch
+    torchvision:
+      version: b919bd0
+      url: https://github.com/pytorch/vision
+    triton:
+      version: a272dfa
+      url: https://github.com/ROCm/triton
+    accelerate:
+      version: b521400f
+      url: https://github.com/huggingface/accelerate
+    aiter:
+      version: de14bec0
+      url: https://github.com/ROCm/aiter
+    diffusers:
+      version: a1f36ee3e
+      url: https://github.com/huggingface/diffusers
+    xfuser:
+      version: adf2681
+      url: https://github.com/xdit-project/xDiT
+    yunchang:
+      version: 2c9b712
+      url: https://github.com/feifeibear/long-context-attention
+  supported_models:
+    - group: Hunyuan Video
+      js_tag: hunyuan
+      models:
+        - model: Hunyuan Video
+          model_repo: tencent/HunyuanVideo
+          revision: refs/pr/18
+          url: https://huggingface.co/tencent/HunyuanVideo
+          github: https://github.com/Tencent-Hunyuan/HunyuanVideo
+          mad_tag: pyt_xdit_hunyuanvideo
+          js_tag: hunyuan_tag
+    - group: Wan-AI
+      js_tag: wan
+      models:
+        - model: Wan2.1
+          model_repo: Wan-AI/Wan2.1-I2V-14B-720P-Diffusers
+          url: https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P-Diffusers
+          github: https://github.com/Wan-Video/Wan2.1
+          mad_tag: pyt_xdit_wan_2_1
+          js_tag: wan_21_tag
+        - model: Wan2.2
+          model_repo: Wan-AI/Wan2.2-I2V-A14B-Diffusers
+          url: https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B-Diffusers
+          github: https://github.com/Wan-Video/Wan2.2
+          mad_tag: pyt_xdit_wan_2_2
+          js_tag: wan_22_tag
+    - group: FLUX
+      js_tag: flux
+      models:
+        - model: FLUX.1
+          model_repo: black-forest-labs/FLUX.1-dev
+          url: https://huggingface.co/black-forest-labs/FLUX.1-dev
+          github: https://github.com/black-forest-labs/flux
+          mad_tag: pyt_xdit_flux
+          js_tag: flux_1_tag
+        - model: FLUX.1 Kontext
+          model_repo: black-forest-labs/FLUX.1-Kontext-dev
+          url: https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev
+          github: https://github.com/black-forest-labs/flux
+          mad_tag: pyt_xdit_flux_kontext
+          js_tag: flux_1_kontext_tag
+        - model: FLUX.2
+          model_repo: black-forest-labs/FLUX.2-dev
+          url: https://huggingface.co/black-forest-labs/FLUX.2-dev
+          github: https://github.com/black-forest-labs/flux2
+          mad_tag: pyt_xdit_flux_2
+          js_tag: flux_2_tag
+    - group: StableDiffusion
+      js_tag: stablediffusion
+      models:
+        - model: stable-diffusion-3.5-large
+          model_repo: stabilityai/stable-diffusion-3.5-large
+          url: https://huggingface.co/stabilityai/stable-diffusion-3.5-large
+          github: https://github.com/Stability-AI/sd3.5
+          mad_tag: pyt_xdit_sd_3_5
+          js_tag: stable_diffusion_3_5_large_tag
@@ -0,0 +1,311 @@
+docker:
+  pull_tag: rocm/pytorch-xdit:v26.2
+  docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-xdit/v26.2/images/sha256-e2c504af438bb9cf60e3869c499baa5102b3d3f62141b99c49743e755ae44008
+  ROCm: 7.11.0
+  whats_new:
+    - "LTX-2 support"
+    - "Flux.2 Klein support"
+    - "Aiter update to support opt_groupnorm, dynamic scaling in FP8 and Sage attention v1"
+  components:
+    TheRock:
+      version: 1728a81
+      url: https://github.com/ROCm/TheRock
+    rccl:
+      version: d23d18f
+      url: https://github.com/ROCm/rccl
+    composable_kernel:
+      version: ab0101c
+      url: https://github.com/ROCm/composable_kernel
+    rocm-libraries:
+      version: a2f7c35
+      url: https://github.com/ROCm/rocm-libraries
+    rocm-systems:
+      version: 659737c
+      url: https://github.com/ROCm/rocm-systems
+    torch:
+      version: 91be249
+      url: https://github.com/ROCm/pytorch
+    torchvision:
+      version: b919bd0
+      url: https://github.com/pytorch/vision
+    triton:
+      version: a272dfa
+      url: https://github.com/ROCm/triton
+    accelerate:
+      version: b7f2212
+      url: https://github.com/huggingface/accelerate
+    aiter:
+      version: 42ae0ad
+      url: https://github.com/ROCm/aiter
+    diffusers:
+      version: a3dcd9
+      url: https://github.com/huggingface/diffusers
+    xfuser:
+      version: 635fc29
+      url: https://github.com/xdit-project/xDiT
+    yunchang:
+      version: 631bdfd
+      url: https://github.com/feifeibear/long-context-attention
+  supported_models:
+    - group: Hunyuan Video
+      js_tag: hunyuan
+      models:
+        - model: Hunyuan Video
+          model_repo: tencent/HunyuanVideo
+          revision: refs/pr/18
+          url: https://huggingface.co/tencent/HunyuanVideo
+          github: https://github.com/Tencent-Hunyuan/HunyuanVideo
+          mad_tag: pyt_xdit_hunyuanvideo
+          js_tag: hunyuan_tag
+          benchmark_command:
+            - mkdir results
+            - 'xdit \'
+            - '--model {model_repo} \'
+            - '--prompt "In the large cage, two puppies were wagging their tails at each other." \'
+            - '--batch_size 1 \'
+            - '--height 720 --width 1280 \'
+            - '--seed 1168860793 \'
+            - '--num_frames 129 \'
+            - '--num_inference_steps 50 \'
+            - '--warmup_calls 1 \'
+            - '--num_iterations 1 \'
+            - '--ulysses_degree 8 \'
+            - '--enable_tiling --enable_slicing \'
+            - '--guidance_scale 6.0 \'
+            - '--use_torch_compile \'
+            - '--attention_backend aiter \'
+            - '--output_directory results'
+        - model: Hunyuan Video 1.5
+          model_repo: hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v
+          url: https://huggingface.co/hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v
+          github: https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5
+          mad_tag: pyt_xdit_hunyuanvideo_1_5
+          js_tag: hunyuan_1_5_tag
+          benchmark_command:
+            - mkdir results
+            - 'xdit \'
+            - '--model {model_repo} \'
+            - '--prompt "In the large cage, two puppies were wagging their tails at each other." \'
+            - '--task t2v \'
+            - '--height 720 --width 1280 \'
+            - '--seed 1168860793 \'
+            - '--num_frames 129 \'
+            - '--num_inference_steps 50 \'
+            - '--num_iterations 1 \'
+            - '--ulysses_degree 8 \'
+            - '--enable_tiling --enable_slicing \'
+            - '--use_torch_compile \'
+            - '--attention_backend aiter \'
+            - '--output_directory results'
+    - group: Wan-AI
+      js_tag: wan
+      models:
+        - model: Wan2.1
+          model_repo: Wan-AI/Wan2.1-I2V-14B-720P-Diffusers
+          url: https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P-Diffusers
+          github: https://github.com/Wan-Video/Wan2.1
+          mad_tag: pyt_xdit_wan_2_1
+          js_tag: wan_21_tag
+          benchmark_command:
+            - mkdir results
+            - 'xdit \'
+            - '--model {model_repo} \'
+            - '--prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline''s intricate details and the refreshing atmosphere of the seaside." \'
+            - '--height 720 \'
+            - '--width 1280 \'
+            - '--input_images /app/data/wan_input.jpg \'
+            - '--num_frames 81 \'
+            - '--ulysses_degree 8 \'
+            - '--seed 42 \'
+            - '--num_iterations 1 \'
+            - '--num_inference_steps 40 \'
+            - '--use_torch_compile \'
+            - '--attention_backend aiter \'
+            - '--output_directory results'
+        - model: Wan2.2
+          model_repo: Wan-AI/Wan2.2-I2V-A14B-Diffusers
+          url: https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B-Diffusers
+          github: https://github.com/Wan-Video/Wan2.2
+          mad_tag: pyt_xdit_wan_2_2
+          js_tag: wan_22_tag
+          benchmark_command:
+            - mkdir results
+            - 'xdit \'
+            - '--model {model_repo} \'
+            - '--prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline''s intricate details and the refreshing atmosphere of the seaside." \'
+            - '--height 720 \'
+            - '--width 1280 \'
+            - '--input_images /app/data/wan_input.jpg \'
+            - '--num_frames 81 \'
+            - '--ulysses_degree 8 \'
+            - '--seed 42 \'
+            - '--num_iterations 1 \'
+            - '--num_inference_steps 40 \'
+            - '--use_torch_compile \'
+            - '--attention_backend aiter \'
+            - '--output_directory results'
+    - group: FLUX
+      js_tag: flux
+      models:
+        - model: FLUX.1
+          model_repo: black-forest-labs/FLUX.1-dev
+          url: https://huggingface.co/black-forest-labs/FLUX.1-dev
+          github: https://github.com/black-forest-labs/flux
+          mad_tag: pyt_xdit_flux
+          js_tag: flux_1_tag
+          benchmark_command:
+            - mkdir results
+            - 'xdit \'
+            - '--model {model_repo} \'
+            - '--seed 42 \'
+            - '--prompt "A small cat" \'
+            - '--height 1024 \'
+            - '--width 1024 \'
+            - '--num_inference_steps 25 \'
+            - '--max_sequence_length 256 \'
+            - '--warmup_calls 5 \'
+            - '--ulysses_degree 8 \'
+            - '--use_torch_compile \'
+            - '--guidance_scale 0.0 \'
+            - '--num_iterations 50 \'
+            - '--attention_backend aiter \'
+            - '--output_directory results'
+        - model: FLUX.1 Kontext
+          model_repo: black-forest-labs/FLUX.1-Kontext-dev
+          url: https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev
+          github: https://github.com/black-forest-labs/flux
+          mad_tag: pyt_xdit_flux_kontext
+          js_tag: flux_1_kontext_tag
+          benchmark_command:
+            - mkdir results
+            - 'xdit \'
+            - '--model {model_repo} \'
+            - '--seed 42 \'
+            - '--prompt "Add a cool hat to the cat" \'
+            - '--height 1024 \'
+            - '--width 1024 \'
+            - '--num_inference_steps 30 \'
+            - '--max_sequence_length 512 \'
+            - '--warmup_calls 5 \'
+            - '--ulysses_degree 8 \'
+            - '--use_torch_compile \'
+            - '--input_images /app/data/flux_cat.png \'
+            - '--guidance_scale 2.5 \'
+            - '--num_iterations 25 \'
+            - '--attention_backend aiter \'
+            - '--output_directory results'
+        - model: FLUX.2
+          model_repo: black-forest-labs/FLUX.2-dev
+          url: https://huggingface.co/black-forest-labs/FLUX.2-dev
+          github: https://github.com/black-forest-labs/flux2
+          mad_tag: pyt_xdit_flux_2
+          js_tag: flux_2_tag
+          benchmark_command:
+            - mkdir results
+            - 'xdit \'
+            - '--model {model_repo} \'
+            - '--seed 42 \'
+            - '--prompt "Add a cool hat to the cat" \'
+            - '--height 1024 \'
+            - '--width 1024 \'
+            - '--num_inference_steps 50 \'
+            - '--max_sequence_length 512 \'
+            - '--warmup_calls 5 \'
+            - '--ulysses_degree 8 \'
+            - '--use_torch_compile \'
+            - '--input_images /app/data/flux_cat.png \'
+            - '--guidance_scale 4.0 \'
+            - '--num_iterations 25 \'
+            - '--attention_backend aiter \'
+            - '--output_directory results'
+        - model: FLUX.2 Klein
+          model_repo: black-forest-labs/FLUX.2-klein-9B
+          url: https://huggingface.co/black-forest-labs/FLUX.2-klein-9B
+          github: https://github.com/black-forest-labs/flux2
+          mad_tag: pyt_xdit_flux_2_klein
+          js_tag: flux_2_klein_tag
+          benchmark_command:
+            - mkdir results
+            - 'xdit \'
+            - '--model {model_repo} \'
+            - '--seed 42 \'
+            - '--prompt "A spectacular sunset over the ocean" \'
+            - '--height 2048 \'
+            - '--width 2048 \'
+            - '--num_inference_steps 4 \'
+            - '--warmup_calls 5 \'
+            - '--ulysses_degree 8 \'
+            - '--use_torch_compile \'
+            - '--guidance_scale 1.0 \'
+            - '--num_iterations 25 \'
+            - '--attention_backend aiter \'
+            - '--output_directory results'
+    - group: StableDiffusion
+      js_tag: stablediffusion
+      models:
+        - model: stable-diffusion-3.5-large
+          model_repo: stabilityai/stable-diffusion-3.5-large
+          url: https://huggingface.co/stabilityai/stable-diffusion-3.5-large
+          github: https://github.com/Stability-AI/sd3.5
+          mad_tag: pyt_xdit_sd_3_5
+          js_tag: stable_diffusion_3_5_large_tag
+          benchmark_command:
+            - mkdir results
+            - 'xdit \'
+            - '--model {model_repo} \'
+            - '--prompt "A capybara holding a sign that reads Hello World" \'
+            - '--num_iterations 50 \'
+            - '--num_inference_steps 28 \'
+            - '--pipefusion_parallel_degree 4 \'
+            - '--use_cfg_parallel \'
+            - '--use_torch_compile \'
+            - '--attention_backend aiter \'
+            - '--output_directory results'
+    - group: Z-Image
+      js_tag: z_image
+      models:
+        - model: Z-Image Turbo
+          model_repo: Tongyi-MAI/Z-Image-Turbo
+          url: https://huggingface.co/Tongyi-MAI/Z-Image-Turbo
+          github: https://github.com/Tongyi-MAI/Z-Image
+          mad_tag: pyt_xdit_z_image_turbo
+          js_tag: z_image_turbo_tag
+          benchmark_command:
+            - mkdir results
+            - 'xdit \'
+            - '--model {model_repo} \'
+            - '--seed 42 \'
+            - '--prompt "A crowded beach" \'
+            - '--height 1088 \'
+            - '--width 1920 \'
+            - '--num_inference_steps 9 \'
+            - '--ulysses_degree 2 \'
+            - '--use_torch_compile \'
+            - '--guidance_scale 0.0 \'
+            - '--num_iterations 50 \'
+            - '--attention_backend aiter \'
+            - '--output_directory results'
+    - group: LTX
+      js_tag: ltx
+      models:
+        - model: LTX-2
+          model_repo: Lightricks/LTX-2
+          url: https://huggingface.co/Lightricks/LTX-2
+          github: https://github.com/Lightricks/LTX-2
+          mad_tag: pyt_xdit_ltx2
+          js_tag: ltx2_tag
+          benchmark_command:
+            - mkdir results
+            - 'xdit \'
+            - '--model {model_repo} \'
+            - '--seed 42 \'
+            - '--prompt "Cinematic action packed shot. The man says silently: \"We need to run.\". The camera zooms in on his mouth then immediately screams: \"NOW!\". The camera zooms back out, he turns around and bolts it." \'
+            - '--height 1088 \'
+            - '--width 1920 \'
+            - '--num_inference_steps 40 \'
+            - '--ulysses_degree 8 \'
+            - '--use_torch_compile \'
+            - '--guidance_scale 4.0 \'
+            - '--num_iterations 1 \'
+            - '--attention_backend aiter \'
+            - '--output_directory results'
@@ -1,13 +1,310 @@
 dockers:
-  - pull_tag: rocm/vllm:rocm7.0.0_vllm_0.10.2_20251006
-    docker_hub_url: https://hub.docker.com/layers/rocm/vllm/rocm7.0.0_vllm_0.10.2_20251006/images/sha256-94fd001964e1cf55c3224a445b1fb5be31a7dac302315255db8422d813edd7f5
+  - pull_tag: rocm/vllm:rocm7.0.0_vllm_0.11.2_20251210
+    docker_hub_url: https://hub.docker.com/layers/rocm/vllm/rocm7.0.0_vllm_0.11.2_20251210/images/sha256-e7f02dd2ce3824959658bc0391296f6158638e3ebce164f6c019c4eca8150ec7
    components:
      ROCm: 7.0.0
-      vLLM: 0.10.2 (0.11.0rc2.dev160+g790d22168.rocm700)
+      vLLM: 0.11.2 (0.11.2.dev673+g839868462.rocm700)
      PyTorch: 2.9.0a0+git1c57644
      hipBLASLt: 1.0.0
    dockerfile:
-      commit: 790d22168820507f3105fef29596549378cfe399
+      commit: 8398684622109c806a35d660647060b0b9910663
+
+configs:
+  default:
+    ## DeepSeek AITER MLA currently only supports --block-size 1
+    - &deepseek-r1-serving
+      benchmark: serving
+      model: deepseek-ai/DeepSeek-R1-0528
+      tp: 8
+      inp: 1024
+      out: 1024
+      dtype: auto
+      max_concurrency: 1 8 32 128
+      extra_args:
+        async-scheduling: True
+        block-size: 1
+    ## gpt-oss requires AITER unified attention and performs best with block-size 64 and FULL_AND_PIECEWISE cudagraph mode
+    - &gpt-oss-120b-serving
+      benchmark: serving
+      model: openai/gpt-oss-120b
+      tp: 8
+      inp: 1024
+      out: 1024
+      dtype: auto
+      max_concurrency: 1 8 32 128
+      env:
+        VLLM_ROCM_USE_AITER_MHA: 0
+        VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION: 1
+      extra_args:
+        async-scheduling: True
+        block-size: 64
+        compilation-config: '{\"cudagraph_mode\":\"FULL_AND_PIECEWISE\"}'
+    - &llama-3-serving
+      benchmark: serving
+      model:
+        meta-llama/Llama-3.1-405B-Instruct
+        amd/Llama-3.1-405B-Instruct-FP8-KV
+        meta-llama/Llama-3.3-70B-Instruct
+        amd/Llama-3.3-70B-Instruct-FP8-KV
+      tp: 8
+      inp: 1024
+      out: 1024
+      dtype: auto
+      max_concurrency: 1 8 32 128
+      extra_args:
+        async-scheduling: True
+      arch_overrides:
+        gfx942:
+          dtype: float16
+    ## Llama 3.x MXFP4 (gfx950 only)
+    - &llama-3-mxfp4-serving
+      benchmark: serving
+      model:
+        amd/Llama-3.1-405B-Instruct-MXFP4-Preview
+        amd/Llama-3.3-70B-Instruct-MXFP4-Preview
+      tp: 8
+      inp: 1024
+      out: 1024
+      dtype: auto
+      max_concurrency: 1 8 32 128
+      extra_args:
+        async-scheduling: True
+    ## Llama 4 currently does not support full cudagraph or attn fusion
+    - &llama-4-fp8-serving
+      benchmark: serving
+      model:
+        meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8
+      tp: 8
+      inp: 1024
+      out: 1024
+      dtype: auto
+      max_concurrency: 1 8 32 128
+      extra_args:
+        async-scheduling: True
+        compilation-config: '{\"cudagraph_mode\":\"PIECEWISE\",\"pass_config\":{\"enable_attn_fusion\":false}}'
+      arch_overrides:
+        gfx942:
+          dtype: float16
+    - &mixtral-8x22b-serving
+      benchmark: serving
+      model:
+        mistralai/Mixtral-8x22B-Instruct-v0.1
+        amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
+      tp: 8
+      inp: 1024
+      out: 1024
+      dtype: auto
+      max_concurrency: 1 8 32 128
+      extra_args:
+        async-scheduling: True
+      arch_overrides:
+        gfx942:
+          dtype: float16
+  extended:
+    ## gpt-oss requires AITER unified attention and performs best with block-size 64 and FULL_AND_PIECEWISE cudagraph mode
+    - &gpt-oss-20b-serving
+      benchmark: serving
+      model:
+        openai/gpt-oss-20b
+      tp: 1
+      inp: 1024
+      out: 1024
+      dtype: auto
+      max_concurrency: 1
+      env:
+        VLLM_ROCM_USE_AITER_MHA: 0
+        VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION: 1
+      extra_args:
+        async-scheduling: True
+        block-size: 64
+        compilation-config: '{\"cudagraph_mode\":\"FULL_AND_PIECEWISE\"}'
+    - &llama-3-8b-phi-4-qwen3-serving
+      benchmark: serving
+      model:
+        meta-llama/Llama-3.1-8B-Instruct
+        amd/Llama-3.1-8B-Instruct-FP8-KV
+        microsoft/phi-4
+        Qwen/Qwen3-8B
+        Qwen/Qwen3-32B
+        Qwen/Qwen3-30B-A3B-Thinking-2507
+        Qwen/Qwen3-30B-A3B-Thinking-2507-FP8
+      tp: 1
+      inp: 1024
+      out: 1024
+      dtype: auto
+      max_concurrency: 1
+      extra_args:
+        async-scheduling: True
+      arch_overrides:
+        gfx942:
+          dtype: float16
+
+    - &llama-2-70b-serving
+      benchmark: serving
+      model:
+        meta-llama/Llama-2-70b-chat-hf
+      tp: 8
+      inp: 1024
+      out: 1024
+      dtype: auto
+      max_concurrency: 1
+      extra_args:
+        async-scheduling: True
+      arch_overrides:
+        gfx942:
+          dtype: float16
+    ## Llama 4 currently does not support full cudagraph or attn fusion
+    - &llama-4-serving
+      benchmark: serving
+      model:
+        meta-llama/Llama-4-Scout-17B-16E-Instruct
+        meta-llama/Llama-4-Maverick-17B-128E-Instruct
+      tp: 8
+      inp: 1024
+      out: 1024
+      dtype: auto
+      max_concurrency: 1
+      extra_args:
+        async-scheduling: True
+        compilation-config: '{\"cudagraph_mode\":\"PIECEWISE\",\"pass_config\":{\"enable_attn_fusion\":false}}'
+      arch_overrides:
+        gfx942:
+          dtype: float16
+
+    - &mixtral-8x7b-serving
+      benchmark: serving
+      model:
+        mistralai/Mixtral-8x7B-Instruct-v0.1
+        amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV
+      tp: 8
+      inp: 1024
+      out: 1024
+      dtype: auto
+      max_concurrency: 1
+      extra_args:
+        async-scheduling: True
+      arch_overrides:
+        gfx942:
+          dtype: float16
+    ## Qwen 235B requires --enable-expert-parallel with tp 8
+    - &qwen3-235b-a22b-serving
+      benchmark: serving
+      model:
+        Qwen/Qwen3-235B-A22B-Thinking-2507
+        Qwen/Qwen3-235B-A22B-Thinking-2507-FP8
+      tp: 8
+      inp: 1024
+      out: 1024
+      dtype: auto
+      max_concurrency: 1
+      extra_args:
+        async-scheduling: True
+        enable-expert-parallel: True
+      arch_overrides:
+        gfx942:
+          dtype: float16
+  accuracy:
+    ## DeepSeek AITER MLA currently only supports --block-size 1
+    - &deepseek-r1-accuracy
+      benchmark: accuracy
+      model: deepseek-ai/DeepSeek-R1-0528
+      tp: 8
+      dtype: auto
+      extra_args:
+        async-scheduling: True
+        block-size: 1
+      bench_args:
+        apply_chat_template: True
+    ## gpt-oss requires AITER unified attention and performs best with block-size 64 and FULL_AND_PIECEWISE cudagraph mode
+    - &gpt-oss-120b-accuracy
+      benchmark: accuracy
+      model: openai/gpt-oss-120b
+      tp: 8
+      dtype: auto
+      env:
+        VLLM_ROCM_USE_AITER_MHA: 0
+        VLLM_USE_AITER_UNIFIED_ATTENTION: 1
+      extra_args:
+        async-scheduling: True
+        block-size: 64
+        compilation-config: '{\"cudagraph_mode\":\"FULL_AND_PIECEWISE\"}'
+      bench_args:
+        apply_chat_template: True
+    ## Llama 3.x bf16 and fp8 perform better with --dtype float16 on gfx942
+    - &llama-3-accuracy
+      benchmark: accuracy
+      model:
+        meta-llama/Llama-3.1-405B-Instruct
+        amd/Llama-3.1-405B-Instruct-FP8-KV
+        meta-llama/Llama-3.3-70B-Instruct
+        amd/Llama-3.3-70B-Instruct-FP8-KV
+      tp: 8
+      dtype: auto
+      extra_args:
+        async-scheduling: True
+      bench_args:
+        apply_chat_template: True
+      arch_overrides:
+        gfx942:
+          dtype: float16
+    ## Llama 3.x MXFP4 (gfx950 only)
+    - &llama-3-mxfp4-accuracy
+      benchmark: accuracy
+      model:
+        amd/Llama-3.1-405B-Instruct-MXFP4-Preview
+        amd/Llama-3.3-70B-Instruct-MXFP4-Preview
+      tp: 8
+      dtype: auto
+      extra_args:
+        async-scheduling: True
+      bench_args:
+        apply_chat_template: True
+    ## Llama 4 currently does not support full cudagraph or attn fusion
+    - &llama-4-fp8-accuracy
+      benchmark: accuracy
+      model:
+        meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8
+      tp: 8
+      dtype: auto
+      extra_args:
+        async-scheduling: True
+        compilation-config: '{\"cudagraph_mode\":\"PIECEWISE\",\"pass_config\":{\"enable_attn_fusion\":false}}'
+      bench_args:
+        apply_chat_template: True
+      arch_overrides:
+        gfx942:
+          dtype: float16
+    ## Mistral models require --tokenizer-mode mistral for correct decoding
+    - &mixtral-8x22b-accuracy
+      benchmark: accuracy
+      model:
+        mistralai/Mixtral-8x22B-Instruct-v0.1
+        amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
+      tp: 8
+      dtype: auto
+      extra_args:
+        async-scheduling: True
+      bench_args:
+        apply_chat_template: True
+      arch_overrides:
+        gfx942:
+          dtype: float16
+    ## Qwen 235B requires --enable-expert-parallel with tp 8
+    - &qwen3-235b-a22b-accuracy
+      benchmark: accuracy
+      model:
+        Qwen/Qwen3-235B-A22B-Thinking-2507
+        Qwen/Qwen3-235B-A22B-Thinking-2507-FP8
+      dtype: auto
+      extra_args:
+        async-scheduling: True
+        enable-expert-parallel: True
+      bench_args:
+        apply_chat_template: True
+      arch_overrides:
+        gfx942:
+          dtype: float16
+
 model_groups:
  - group: Meta Llama
    tag: llama
@@ -18,132 +315,139 @@ model_groups:
        url: https://huggingface.co/meta-llama/Llama-2-70b-chat-hf
        precision: float16
        config:
-          tp: 8
-          dtype: auto
-          kv_cache_dtype: auto
-          max_num_batched_tokens: 4096
-          max_model_len: 4096
+          serving: *llama-2-70b-serving
+          ex:
+            kv_cache_dtype: auto
+            max_num_batched_tokens: 4096
+            max_model_len: 4096
      - model: Llama 3.1 8B
        mad_tag: pyt_vllm_llama-3.1-8b
        model_repo: meta-llama/Llama-3.1-8B-Instruct
        url: https://huggingface.co/meta-llama/Llama-3.1-8B
        precision: float16
        config:
-          tp: 1
-          dtype: auto
-          kv_cache_dtype: auto
-          max_num_batched_tokens: 131072
-          max_model_len: 8192
+          serving: *llama-3-8b-phi-4-qwen3-serving
+          ex:
+            kv_cache_dtype: auto
+            max_num_batched_tokens: 131072
+            max_model_len: 8192
      - model: Llama 3.1 8B FP8
        mad_tag: pyt_vllm_llama-3.1-8b_fp8
        model_repo: amd/Llama-3.1-8B-Instruct-FP8-KV
        url: https://huggingface.co/amd/Llama-3.1-8B-Instruct-FP8-KV
        precision: float8
        config:
-          tp: 1
-          dtype: auto
-          kv_cache_dtype: fp8
-          max_num_batched_tokens: 131072
-          max_model_len: 8192
+          serving: *llama-3-8b-phi-4-qwen3-serving
+          ex:
+            kv_cache_dtype: fp8
+            max_num_batched_tokens: 131072
+            max_model_len: 8192
      - model: Llama 3.1 405B
        mad_tag: pyt_vllm_llama-3.1-405b
        model_repo: meta-llama/Llama-3.1-405B-Instruct
        url: https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct
        precision: float16
        config:
-          tp: 8
-          dtype: auto
-          kv_cache_dtype: auto
-          max_num_batched_tokens: 131072
-          max_model_len: 8192
+          serving: *llama-3-serving
+          accuracy: *llama-3-accuracy
+          ex:
+            kv_cache_dtype: auto
+            max_num_batched_tokens: 131072
+            max_model_len: 8192
      - model: Llama 3.1 405B FP8
        mad_tag: pyt_vllm_llama-3.1-405b_fp8
        model_repo: amd/Llama-3.1-405B-Instruct-FP8-KV
        url: https://huggingface.co/amd/Llama-3.1-405B-Instruct-FP8-KV
        precision: float8
        config:
-          tp: 8
-          dtype: auto
-          kv_cache_dtype: fp8
-          max_num_batched_tokens: 131072
-          max_model_len: 8192
+          serving: *llama-3-serving
+          accuracy: *llama-3-accuracy
+          ex:
+            kv_cache_dtype: fp8
+            max_num_batched_tokens: 131072
+            max_model_len: 8192
      - model: Llama 3.1 405B MXFP4
        mad_tag: pyt_vllm_llama-3.1-405b_fp4
        model_repo: amd/Llama-3.1-405B-Instruct-MXFP4-Preview
        url: https://huggingface.co/amd/Llama-3.1-405B-Instruct-MXFP4-Preview
        precision: float4
        config:
-          tp: 8
-          dtype: auto
-          kv_cache_dtype: fp8
-          max_num_batched_tokens: 131072
-          max_model_len: 8192
+          serving: *llama-3-mxfp4-serving
+          accuracy: *llama-3-mxfp4-accuracy
+          ex:
+            kv_cache_dtype: fp8
+            max_num_batched_tokens: 131072
+            max_model_len: 8192
      - model: Llama 3.3 70B
        mad_tag: pyt_vllm_llama-3.3-70b
        model_repo: meta-llama/Llama-3.3-70B-Instruct
        url: https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct
        precision: float16
        config:
-          tp: 8
-          dtype: auto
-          kv_cache_dtype: auto
-          max_num_batched_tokens: 131072
-          max_model_len: 8192
+          serving: *llama-3-serving
+          accuracy: *llama-3-accuracy
+          ex:
+            kv_cache_dtype: auto
+            max_num_batched_tokens: 131072
+            max_model_len: 8192
      - model: Llama 3.3 70B FP8
        mad_tag: pyt_vllm_llama-3.3-70b_fp8
        model_repo: amd/Llama-3.3-70B-Instruct-FP8-KV
        url: https://huggingface.co/amd/Llama-3.3-70B-Instruct-FP8-KV
        precision: float8
        config:
-          tp: 8
-          dtype: auto
-          kv_cache_dtype: fp8
-          max_num_batched_tokens: 131072
-          max_model_len: 8192
+          serving: *llama-3-serving
+          accuracy: *llama-3-accuracy
+          ex:
+            kv_cache_dtype: fp8
+            max_num_batched_tokens: 131072
+            max_model_len: 8192
      - model: Llama 3.3 70B MXFP4
        mad_tag: pyt_vllm_llama-3.3-70b_fp4
        model_repo: amd/Llama-3.3-70B-Instruct-MXFP4-Preview
        url: https://huggingface.co/amd/Llama-3.3-70B-Instruct-MXFP4-Preview
        precision: float4
        config:
-          tp: 8
-          dtype: auto
-          kv_cache_dtype: fp8
-          max_num_batched_tokens: 131072
-          max_model_len: 8192
+          serving: *llama-3-mxfp4-serving
+          accuracy: *llama-3-mxfp4-accuracy
+          ex:
+            kv_cache_dtype: fp8
+            max_num_batched_tokens: 131072
+            max_model_len: 8192
      - model: Llama 4 Scout 17Bx16E
        mad_tag: pyt_vllm_llama-4-scout-17b-16e
        model_repo: meta-llama/Llama-4-Scout-17B-16E-Instruct
        url: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct
        precision: float16
        config:
-          tp: 8
-          dtype: auto
-          kv_cache_dtype: auto
-          max_num_batched_tokens: 32768
-          max_model_len: 8192
+          serving: *llama-4-serving
+          ex:
+            kv_cache_dtype: auto
+            max_num_batched_tokens: 32768
+            max_model_len: 8192
      - model: Llama 4 Maverick 17Bx128E
        mad_tag: pyt_vllm_llama-4-maverick-17b-128e
        model_repo: meta-llama/Llama-4-Maverick-17B-128E-Instruct
        url: https://huggingface.co/meta-llama/Llama-4-Maverick-17B-128E-Instruct
        precision: float16
        config:
-          tp: 8
-          dtype: auto
-          kv_cache_dtype: auto
-          max_num_batched_tokens: 32768
-          max_model_len: 8192
+          serving: *llama-4-serving
+          ex:
+            kv_cache_dtype: auto
+            max_num_batched_tokens: 32768
+            max_model_len: 8192
      - model: Llama 4 Maverick 17Bx128E FP8
        mad_tag: pyt_vllm_llama-4-maverick-17b-128e_fp8
        model_repo: meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8
        url: https://huggingface.co/meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8
        precision: float8
        config:
-          tp: 8
-          dtype: auto
-          kv_cache_dtype: fp8
-          max_num_batched_tokens: 131072
-          max_model_len: 8192
+          serving: *llama-4-fp8-serving
+          accuracy: *llama-4-fp8-accuracy
+          ex:
+            kv_cache_dtype: fp8
+            max_num_batched_tokens: 131072
+            max_model_len: 8192
  - group: DeepSeek
    tag: deepseek
    models:
@@ -153,12 +457,12 @@ model_groups:
        url: https://huggingface.co/deepseek-ai/DeepSeek-R1-0528
        precision: float8
        config:
-          tp: 8
-          dtype: auto
-          kv_cache_dtype: fp8
-          max_num_seqs: 1024
-          max_num_batched_tokens: 131072
-          max_model_len: 8192
+          serving: *deepseek-r1-serving
+          accuracy: *deepseek-r1-accuracy
+          ex:
+            kv_cache_dtype: fp8
+            max_num_batched_tokens: 131072
+            max_model_len: 8192
  - group: OpenAI GPT OSS
    tag: gpt-oss
    models:
@@ -168,22 +472,23 @@ model_groups:
        url: https://huggingface.co/openai/gpt-oss-20b
        precision: bfloat16
        config:
-          tp: 1
-          dtype: auto
-          kv_cache_dtype: auto
-          max_num_batched_tokens: 8192
-          max_model_len: 8192
+          serving: *gpt-oss-20b-serving
+          ex:
+            kv_cache_dtype: auto
+            max_num_batched_tokens: 8192
+            max_model_len: 8192
      - model: GPT OSS 120B
        mad_tag: pyt_vllm_gpt-oss-120b
        model_repo: openai/gpt-oss-120b
        url: https://huggingface.co/openai/gpt-oss-120b
        precision: bfloat16
        config:
-          tp: 8
-          dtype: auto
-          kv_cache_dtype: auto
-          max_num_batched_tokens: 8192
-          max_model_len: 8192
+          serving: *gpt-oss-120b-serving
+          accuracy: *gpt-oss-120b-accuracy
+          ex:
+            kv_cache_dtype: auto
+            max_num_batched_tokens: 8192
+            max_model_len: 8192
  - group: Mistral AI
    tag: mistral
    models:
@@ -193,44 +498,46 @@ model_groups:
        url: https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1
        precision: float16
        config:
-          tp: 8
-          dtype: auto
-          kv_cache_dtype: auto
-          max_num_batched_tokens: 32768
-          max_model_len: 8192
+          serving: *mixtral-8x7b-serving
+          ex:
+            kv_cache_dtype: auto
+            max_num_batched_tokens: 32768
+            max_model_len: 8192
      - model: Mixtral MoE 8x7B FP8
        mad_tag: pyt_vllm_mixtral-8x7b_fp8
        model_repo: amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV
        url: https://huggingface.co/amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV
        precision: float8
        config:
-          tp: 8
-          dtype: auto
-          kv_cache_dtype: fp8
-          max_num_batched_tokens: 32768
-          max_model_len: 8192
+          serving: *mixtral-8x7b-serving
+          ex:
+            kv_cache_dtype: fp8
+            max_num_batched_tokens: 32768
+            max_model_len: 8192
      - model: Mixtral MoE 8x22B
        mad_tag: pyt_vllm_mixtral-8x22b
        model_repo: mistralai/Mixtral-8x22B-Instruct-v0.1
        url: https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1
        precision: float16
        config:
-          tp: 8
-          dtype: auto
-          kv_cache_dtype: auto
-          max_num_batched_tokens: 65536
-          max_model_len: 8192
+          serving: *mixtral-8x22b-serving
+          accuracy: *mixtral-8x22b-accuracy
+          ex:
+            kv_cache_dtype: auto
+            max_num_batched_tokens: 65536
+            max_model_len: 8192
      - model: Mixtral MoE 8x22B FP8
        mad_tag: pyt_vllm_mixtral-8x22b_fp8
        model_repo: amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
        url: https://huggingface.co/amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
        precision: float8
        config:
-          tp: 8
-          dtype: auto
-          kv_cache_dtype: fp8
-          max_num_batched_tokens: 65536
-          max_model_len: 8192
+          serving: *mixtral-8x22b-serving
+          accuracy: *mixtral-8x22b-accuracy
+          ex:
+            kv_cache_dtype: fp8
+            max_num_batched_tokens: 65536
+            max_model_len: 8192
  - group: Qwen
    tag: qwen
    models:
@@ -240,66 +547,68 @@ model_groups:
        url: https://huggingface.co/Qwen/Qwen3-8B
        precision: float16
        config:
-          tp: 1
-          dtype: auto
-          kv_cache_dtype: auto
-          max_num_batched_tokens: 40960
-          max_model_len: 8192
+          serving: *llama-3-8b-phi-4-qwen3-serving
+          ex:
+            kv_cache_dtype: auto
+            max_num_batched_tokens: 40960
+            max_model_len: 8192
      - model: Qwen3 32B
        mad_tag: pyt_vllm_qwen3-32b
-        model_repo: Qwen/Qwen3-32b
+        model_repo: Qwen/Qwen3-32B
        url: https://huggingface.co/Qwen/Qwen3-32B
        precision: float16
        config:
-          tp: 1
-          dtype: auto
-          kv_cache_dtype: auto
-          max_num_batched_tokens: 40960
-          max_model_len: 8192
-      - model: Qwen3 30B A3B
+          serving: *llama-3-8b-phi-4-qwen3-serving
+          ex:
+            kv_cache_dtype: auto
+            max_num_batched_tokens: 40960
+            max_model_len: 8192
+      - model: Qwen3 30B A3B Thinking
        mad_tag: pyt_vllm_qwen3-30b-a3b
-        model_repo: Qwen/Qwen3-30B-A3B
-        url: https://huggingface.co/Qwen/Qwen3-30B-A3B
+        model_repo: Qwen/Qwen3-30B-A3B-Thinking-2507
+        url: https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507
        precision: float16
        config:
-          tp: 1
-          dtype: auto
-          kv_cache_dtype: auto
-          max_num_batched_tokens: 40960
-          max_model_len: 8192
-      - model: Qwen3 30B A3B FP8
+          serving: *llama-3-8b-phi-4-qwen3-serving
+          ex:
+            kv_cache_dtype: auto
+            max_num_batched_tokens: 40960
+            max_model_len: 8192
+      - model: Qwen3 30B A3B Thinking FP8
        mad_tag: pyt_vllm_qwen3-30b-a3b_fp8
-        model_repo: Qwen/Qwen3-30B-A3B-FP8
-        url: https://huggingface.co/Qwen/Qwen3-30B-A3B-FP8
+        model_repo: Qwen/Qwen3-30B-A3B-Thinking-2507-FP8
+        url: https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507-FP8
        precision: float16
        config:
-          tp: 1
-          dtype: auto
-          kv_cache_dtype: fp8
-          max_num_batched_tokens: 40960
-          max_model_len: 8192
-      - model: Qwen3 235B A22B
+          serving: *llama-3-8b-phi-4-qwen3-serving
+          ex:
+            kv_cache_dtype: auto
+            max_num_batched_tokens: 40960
+            max_model_len: 8192
+      - model: Qwen3 235B A22B Thinking
        mad_tag: pyt_vllm_qwen3-235b-a22b
-        model_repo: Qwen/Qwen3-235B-A22B
-        url: https://huggingface.co/Qwen/Qwen3-235B-A22B
+        model_repo: Qwen/Qwen3-235B-A22B-Thinking-2507
+        url: https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507
        precision: float16
        config:
-          tp: 8
-          dtype: auto
-          kv_cache_dtype: auto
-          max_num_batched_tokens: 40960
-          max_model_len: 8192
-      - model: Qwen3 235B A22B FP8
+          serving: *qwen3-235b-a22b-serving
+          accuracy: *qwen3-235b-a22b-accuracy
+          ex:
+            kv_cache_dtype: auto
+            max_num_batched_tokens: 40960
+            max_model_len: 8192
+      - model: Qwen3 235B A22B Thinking FP8
        mad_tag: pyt_vllm_qwen3-235b-a22b_fp8
-        model_repo: Qwen/Qwen3-235B-A22B-FP8
-        url: https://huggingface.co/Qwen/Qwen3-235B-A22B-FP8
+        model_repo: Qwen/Qwen3-235B-A22B-Thinking-2507-FP8
+        url: https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507-FP8
        precision: float8
        config:
-          tp: 8
-          dtype: auto
-          kv_cache_dtype: fp8
-          max_num_batched_tokens: 40960
-          max_model_len: 8192
+          serving: *qwen3-235b-a22b-serving
+          accuracy: *qwen3-235b-a22b-accuracy
+          ex:
+            kv_cache_dtype: auto
+            max_num_batched_tokens: 40960
+            max_model_len: 8192
  - group: Microsoft Phi
    tag: phi
    models:
@@ -309,8 +618,8 @@ model_groups:
        url: https://huggingface.co/microsoft/phi-4
        precision: float16
        config:
-          tp: 1
-          dtype: auto
-          kv_cache_dtype: auto
-          max_num_batched_tokens: 16384
-          max_model_len: 8192
+          serving: *llama-3-8b-phi-4-qwen3-serving
+          ex:
+            kv_cache_dtype: auto
+            max_num_batched_tokens: 16384
+            max_model_len: 8192
@@ -0,0 +1,354 @@
+docker:
+  pull_tag: rocm/pytorch-xdit:v26.3
+  docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-xdit/v26.3/images/sha256-ac78a03d2911bf1b49c001d3be2e8bd745c1bc455cb49ae972825a7986880902
+  ROCm: 7.12.0
+  whats_new:
+    - "Qwen-Image support"
+    - "Qwen-Image-Edit support"
+    - "Aiter update to support Sage attention v2"
+    - "xDiT update to support MXFP4 GEMMs in Wan2.2, Wan2.1 and Flux.2"
+  components:
+    TheRock: 
+      version: e40a6da
+      url: https://github.com/ROCm/TheRock
+    rocm-libraries:
+      version: 9e9e900
+      url: https://github.com/ROCm/rocm-libraries
+    rocm-systems:
+      version: ca89a1a
+      url: https://github.com/ROCm/rocm-systems
+    torch:
+      version: 91be249
+      url: https://github.com/ROCm/pytorch
+    torchaudio:
+      version: e3c6ee2
+      url: https://github.com/pytorch/audio
+    torchvision:
+      version: b919bd0
+      url: https://github.com/pytorch/vision
+    triton:
+      version: a272dfa
+      url: https://github.com/ROCm/triton
+    accelerate:
+      version: 46ba481
+      url: https://github.com/huggingface/accelerate
+    aiter:
+      version: 82d733f
+      url: https://github.com/ROCm/aiter
+    diffusers:
+      version: a80b192
+      url: https://github.com/huggingface/diffusers
+    xfuser:
+      version: 2882027
+      url: https://github.com/xdit-project/xDiT
+    yunchang:
+      version: 631bdfd
+      url: https://github.com/feifeibear/long-context-attention
+  supported_models:
+    - group: Hunyuan Video
+      js_tag: hunyuan
+      models:
+        - model: Hunyuan Video
+          model_repo: tencent/HunyuanVideo
+          revision: refs/pr/18
+          url: https://huggingface.co/tencent/HunyuanVideo
+          github: https://github.com/Tencent-Hunyuan/HunyuanVideo
+          mad_tag: pyt_xdit_hunyuanvideo
+          js_tag: hunyuan_tag
+          benchmark_command:
+            - mkdir results
+            - 'xdit \'
+            - '--model {model_repo} \'
+            - '--prompt "In the large cage, two puppies were wagging their tails at each other." \'
+            - '--batch_size 1 \'
+            - '--height 720 --width 1280 \'
+            - '--seed 1168860793 \'
+            - '--num_frames 129 \'
+            - '--num_inference_steps 50 \'
+            - '--warmup_calls 1 \'
+            - '--num_iterations 1 \'
+            - '--ulysses_degree 8 \'
+            - '--enable_tiling --enable_slicing \'
+            - '--guidance_scale 6.0 \'
+            - '--use_torch_compile \'
+            - '--attention_backend aiter \'
+            - '--output_directory results'
+        - model: Hunyuan Video 1.5
+          model_repo: hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v
+          url: https://huggingface.co/hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v
+          github: https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5
+          mad_tag: pyt_xdit_hunyuanvideo_1_5
+          js_tag: hunyuan_1_5_tag
+          benchmark_command:
+            - mkdir results
+            - 'xdit \'
+            - '--model {model_repo} \'
+            - '--prompt "In the large cage, two puppies were wagging their tails at each other." \'
+            - '--task t2v \'
+            - '--height 720 --width 1280 \'
+            - '--seed 1168860793 \'
+            - '--num_frames 129 \'
+            - '--num_inference_steps 50 \'
+            - '--num_iterations 1 \'
+            - '--ulysses_degree 8 \'
+            - '--enable_tiling --enable_slicing \'
+            - '--use_torch_compile \'
+            - '--attention_backend aiter \'
+            - '--output_directory results'
+    - group: Wan-AI
+      js_tag: wan
+      models:
+        - model: Wan2.1
+          model_repo: Wan-AI/Wan2.1-I2V-14B-720P-Diffusers
+          url: https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P-Diffusers
+          github: https://github.com/Wan-Video/Wan2.1
+          mad_tag: pyt_xdit_wan_2_1
+          js_tag: wan_21_tag
+          benchmark_command:
+            - mkdir results
+            - 'xdit \'
+            - '--model {model_repo} \'
+            - '--prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline''s intricate details and the refreshing atmosphere of the seaside." \'
+            - '--height 720 \'
+            - '--width 1280 \'
+            - '--input_images /app/data/wan_input.jpg \'
+            - '--num_frames 81 \'
+            - '--ulysses_degree 8 \'
+            - '--seed 42 \'
+            - '--num_iterations 1 \'
+            - '--num_inference_steps 40 \'
+            - '--use_torch_compile \'
+            - '--attention_backend aiter \'
+            - '--output_directory results'
+        - model: Wan2.2
+          model_repo: Wan-AI/Wan2.2-I2V-A14B-Diffusers
+          url: https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B-Diffusers
+          github: https://github.com/Wan-Video/Wan2.2
+          mad_tag: pyt_xdit_wan_2_2
+          js_tag: wan_22_tag
+          benchmark_command:
+            - mkdir results
+            - 'xdit \'
+            - '--model {model_repo} \'
+            - '--prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline''s intricate details and the refreshing atmosphere of the seaside." \'
+            - '--height 720 \'
+            - '--width 1280 \'
+            - '--input_images /app/data/wan_input.jpg \'
+            - '--num_frames 81 \'
+            - '--ulysses_degree 8 \'
+            - '--seed 42 \'
+            - '--num_iterations 1 \'
+            - '--num_inference_steps 40 \'
+            - '--use_torch_compile \'
+            - '--attention_backend aiter \'
+            - '--output_directory results'
+    - group: FLUX
+      js_tag: flux
+      models:
+        - model: FLUX.1
+          model_repo: black-forest-labs/FLUX.1-dev
+          url: https://huggingface.co/black-forest-labs/FLUX.1-dev
+          github: https://github.com/black-forest-labs/flux
+          mad_tag: pyt_xdit_flux
+          js_tag: flux_1_tag
+          benchmark_command:
+            - mkdir results
+            - 'xdit \'
+            - '--model {model_repo} \'
+            - '--seed 42 \'
+            - '--prompt "A small cat" \'
+            - '--height 1024 \'
+            - '--width 1024 \'
+            - '--num_inference_steps 25 \'
+            - '--max_sequence_length 256 \'
+            - '--warmup_calls 5 \'
+            - '--ulysses_degree 8 \'
+            - '--use_torch_compile \'
+            - '--guidance_scale 0.0 \'
+            - '--num_iterations 50 \'
+            - '--attention_backend aiter \'
+            - '--output_directory results'
+        - model: FLUX.1 Kontext
+          model_repo: black-forest-labs/FLUX.1-Kontext-dev
+          url: https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev
+          github: https://github.com/black-forest-labs/flux
+          mad_tag: pyt_xdit_flux_kontext
+          js_tag: flux_1_kontext_tag
+          benchmark_command:
+            - mkdir results
+            - 'xdit \'
+            - '--model {model_repo} \'
+            - '--seed 42 \'
+            - '--prompt "Add a cool hat to the cat" \'
+            - '--height 1024 \'
+            - '--width 1024 \'
+            - '--num_inference_steps 30 \'
+            - '--max_sequence_length 512 \'
+            - '--warmup_calls 5 \'
+            - '--ulysses_degree 8 \'
+            - '--use_torch_compile \'
+            - '--input_images /app/data/flux_cat.png \'
+            - '--guidance_scale 2.5 \'
+            - '--num_iterations 25 \'
+            - '--attention_backend aiter \'
+            - '--output_directory results'
+        - model: FLUX.2
+          model_repo: black-forest-labs/FLUX.2-dev
+          url: https://huggingface.co/black-forest-labs/FLUX.2-dev
+          github: https://github.com/black-forest-labs/flux2
+          mad_tag: pyt_xdit_flux_2
+          js_tag: flux_2_tag
+          benchmark_command:
+            - mkdir results
+            - 'xdit \'
+            - '--model {model_repo} \'
+            - '--seed 42 \'
+            - '--prompt "Add a cool hat to the cat" \'
+            - '--height 1024 \'
+            - '--width 1024 \'
+            - '--num_inference_steps 50 \'
+            - '--max_sequence_length 512 \'
+            - '--warmup_calls 5 \'
+            - '--ulysses_degree 8 \'
+            - '--use_torch_compile \'
+            - '--input_images /app/data/flux_cat.png \'
+            - '--guidance_scale 4.0 \'
+            - '--num_iterations 25 \'
+            - '--attention_backend aiter \'
+            - '--output_directory results'
+        - model: FLUX.2 Klein
+          model_repo: black-forest-labs/FLUX.2-klein-9B
+          url: https://huggingface.co/black-forest-labs/FLUX.2-klein-9B
+          github: https://github.com/black-forest-labs/flux2
+          mad_tag: pyt_xdit_flux_2_klein
+          js_tag: flux_2_klein_tag
+          benchmark_command:
+            - mkdir results
+            - 'xdit \'
+            - '--model {model_repo} \'
+            - '--seed 42 \'
+            - '--prompt "A spectacular sunset over the ocean" \'
+            - '--height 2048 \'
+            - '--width 2048 \'
+            - '--num_inference_steps 4 \'
+            - '--warmup_calls 5 \'
+            - '--ulysses_degree 8 \'
+            - '--use_torch_compile \'
+            - '--guidance_scale 1.0 \'
+            - '--num_iterations 25 \'
+            - '--attention_backend aiter \'
+            - '--output_directory results'
+    - group: StableDiffusion
+      js_tag: stablediffusion
+      models:
+        - model: stable-diffusion-3.5-large
+          model_repo: stabilityai/stable-diffusion-3.5-large
+          url: https://huggingface.co/stabilityai/stable-diffusion-3.5-large
+          github: https://github.com/Stability-AI/sd3.5
+          mad_tag: pyt_xdit_sd_3_5
+          js_tag: stable_diffusion_3_5_large_tag
+          benchmark_command:
+            - mkdir results
+            - 'xdit \'
+            - '--model {model_repo} \'
+            - '--prompt "A capybara holding a sign that reads Hello World" \'
+            - '--num_iterations 50 \'
+            - '--num_inference_steps 28 \'
+            - '--pipefusion_parallel_degree 4 \'
+            - '--use_cfg_parallel \'
+            - '--use_torch_compile \'
+            - '--attention_backend aiter \'
+            - '--output_directory results'
+    - group: Z-Image
+      js_tag: z_image
+      models:
+        - model: Z-Image Turbo
+          model_repo: Tongyi-MAI/Z-Image-Turbo
+          url: https://huggingface.co/Tongyi-MAI/Z-Image-Turbo
+          github: https://github.com/Tongyi-MAI/Z-Image
+          mad_tag: pyt_xdit_z_image_turbo
+          js_tag: z_image_turbo_tag
+          benchmark_command:
+            - mkdir results
+            - 'xdit \'
+            - '--model {model_repo} \'
+            - '--seed 42 \'
+            - '--prompt "A crowded beach" \'
+            - '--height 1088 \'
+            - '--width 1920 \'
+            - '--num_inference_steps 9 \'
+            - '--ulysses_degree 2 \'
+            - '--use_torch_compile \'
+            - '--guidance_scale 0.0 \'
+            - '--num_iterations 50 \'
+            - '--attention_backend aiter \'
+            - '--output_directory results'
+    - group: LTX
+      js_tag: ltx
+      models:
+        - model: LTX-2
+          model_repo: Lightricks/LTX-2
+          url: https://huggingface.co/Lightricks/LTX-2
+          github: https://github.com/Lightricks/LTX-2
+          mad_tag: pyt_xdit_ltx2
+          js_tag: ltx2_tag
+          benchmark_command:
+            - mkdir results
+            - 'xdit \'
+            - '--model {model_repo} \'
+            - '--seed 42 \'
+            - '--prompt "Cinematic action packed shot. The man says silently: \"We need to run.\". The camera zooms in on his mouth then immediately screams: \"NOW!\". The camera zooms back out, he turns around and bolts it." \'
+            - '--height 1088 \'
+            - '--width 1920 \'
+            - '--num_inference_steps 40 \'
+            - '--ulysses_degree 8 \'
+            - '--use_torch_compile \'
+            - '--guidance_scale 4.0 \'
+            - '--num_iterations 1 \'
+            - '--attention_backend aiter \'
+            - '--output_directory results'
+    - group: Qwen-Image
+      js_tag: qwen_image
+      models:
+        - model: Qwen-Image
+          model_repo: Qwen/Qwen-Image
+          url: https://huggingface.co/Qwen/Qwen-Image
+          github: https://github.com/QwenLM/Qwen-Image
+          mad_tag: pyt_xdit_qwen_image
+          js_tag: qwen_image_tag
+          benchmark_command:
+            - mkdir results
+            - 'xdit \'
+            - '--model {model_repo} \'
+            - '--seed 42 \'
+            - '--prompt "A cat holding a sign that says hello world" \'
+            - '--height 2048 \'
+            - '--width 2048 \'
+            - '--num_inference_steps 50 \'
+            - '--ulysses_degree 8 \'
+            - '--use_torch_compile \'
+            - '--num_iterations 1 \'
+            - '--attention_backend aiter \'
+            - '--output_directory results'
+        - model: Qwen-Image-Edit
+          model_repo: Qwen/Qwen-Image-Edit
+          url: https://huggingface.co/Qwen/Qwen-Image-Edit
+          github: https://github.com/QwenLM/Qwen-Image
+          mad_tag: pyt_xdit_qwen_image_edit
+          js_tag: qwen_image_edit_tag
+          benchmark_command:
+            - mkdir results
+            - 'xdit \'
+            - '--model {model_repo} \'
+            - '--seed 42 \'
+            - '--prompt "Add a cool hat to the cat." \'
+            - '--negative_prompt "" \'
+            - '--input_images /app/data/flux_cat.png \'
+            - '--height 2048 \'
+            - '--width 2048 \'
+            - '--num_inference_steps 50 \'
+            - '--ulysses_degree 8 \'
+            - '--use_torch_compile \'
+            - '--num_iterations 1 \'
+            - '--attention_backend aiter \'
+            - '--output_directory results'
@@ -1,12 +1,12 @@
 dockers:
-  - pull_tag: rocm/jax-training:maxtext-v25.9
-    docker_hub_url: https://hub.docker.com/layers/rocm/jax-training/maxtext-v25.7/images/sha256-45f4c727d4019a63fc47313d3a5f5a5105569539294ddfd2d742218212ae9025
+  - pull_tag: rocm/jax-training:maxtext-v26.2
+    docker_hub_url: https://hub.docker.com/layers/rocm/jax-training/maxtext-v26.2/images/sha256-a89643388487b1e2fc6b6ef7bd3c44378c05d217309c977a1c18c72d05ebcaeb
    components:
-      ROCm: 7.0.0
-      JAX: 0.6.2
-      Python: 3.10.18
-      Transformer Engine: 2.2.0.dev0+c91bac54
-      hipBLASLt: 1.x.x
+      ROCm: 7.1.1
+      JAX: 0.8.2
+      Python: 3.12
+      Transformer Engine: 2.8.0.dev0+aec00a7f
+      hipBLASLt: 1.2.x
 model_groups:
  - group: Meta Llama
    tag: llama
@@ -15,21 +15,33 @@ model_groups:
        mad_tag: jax_maxtext_train_llama-2-7b
        model_repo: Llama-2-7B
        precision: bf16
-        multinode_training_script: llama2_7b_multinode.sh
+        primus_config_name: llama2_7B-pretrain.yaml
+        multinode_config:
+          gfx950: env_scripts/gfx950_llama2_7b.yml
+          gfx942: env_scripts/llama2_7b.yml
        doc_options: ["single-node", "multi-node"]
      - model: Llama 2 70B
        mad_tag: jax_maxtext_train_llama-2-70b
        model_repo: Llama-2-70B
        precision: bf16
-        multinode_training_script: llama2_70b_multinode.sh
+        primus_config_name: llama2_70B-pretrain.yaml
+        multinode_config:
+          gfx950: env_scripts/gfx950_llama2_70b.yml
+          gfx942: env_scripts/llama2_70b.yml
        doc_options: ["single-node", "multi-node"]
-      - model: Llama 3 8B (multi-node)
+      - model: Llama 3 8B
        mad_tag: jax_maxtext_train_llama-3-8b
-        multinode_training_script: llama3_8b_multinode.sh
+        primus_config_name: llama3_8B-pretrain.yaml
+        multinode_config:
+          gfx950: env_scripts/gfx950_llama3_8b.yml
+          gfx942: env_scripts/llama3_8b.yml
        doc_options: ["multi-node"]
-      - model: Llama 3 70B (multi-node)
+      - model: Llama 3 70B
        mad_tag: jax_maxtext_train_llama-3-70b
-        multinode_training_script: llama3_70b_multinode.sh
+        primus_config_name: llama3_70B-pretrain.yaml
+        multinode_config:
+          gfx950: env_scripts/gfx950_llama3_70b.yml
+          gfx942: env_scripts/llama3_70b.yml
        doc_options: ["multi-node"]
      - model: Llama 3.1 8B
        mad_tag: jax_maxtext_train_llama-3.1-8b
@@ -41,11 +53,22 @@ model_groups:
        model_repo: Llama-3.1-70B
        precision: bf16
        doc_options: ["single-node"]
+      - model: Llama 3.1 405B (multi-node)
+        mad_tag: jax_maxtext_train_llama-3.1-405b
+        model_repo: Llama-3.1-405B
+        precision: bf16
+        multinode_config:
+          gfx950: env_scripts/gfx950_llama3_405b.yml
+        doc_options: ["multi-node"]
      - model: Llama 3.3 70B
        mad_tag: jax_maxtext_train_llama-3.3-70b
        model_repo: Llama-3.3-70B
        precision: bf16
-        doc_options: ["single-node"]
+        primus_config_name: llama3.3_70B-pretrain.yaml
+        multinode_config:
+          gfx950: env_scripts/gfx950_llama3.3_70b.yml
+          gfx942: env_scripts/llama3.3_70b.yml
+        doc_options: ["single-node", "multi-node"]
  - group: DeepSeek
    tag: deepseek
    models:
@@ -53,7 +76,11 @@ model_groups:
        mad_tag: jax_maxtext_train_deepseek-v2-lite-16b
        model_repo: DeepSeek-V2-lite
        precision: bf16
-        doc_options: ["single-node"]
+        primus_config_name: deepseek_v2_16B-pretrain.yaml
+        multinode_config:
+          gfx950: env_scripts/gfx950_deepseek2_16b.yml
+          gfx942: env_scripts/deepseek2_16b.yml
+        doc_options: ["single-node", "multi-node"]
  - group: Mistral AI
    tag: mistral
    models:
@@ -61,4 +88,8 @@ model_groups:
        mad_tag: jax_maxtext_train_mixtral-8x7b
        model_repo: Mixtral-8x7B
        precision: bf16
-        doc_options: ["single-node"]
+        primus_config_name: mixtral_8x7B-pretrain.yaml
+        multinode_config:
+          gfx950: env_scripts/gfx950_mixtral_8x7b.yml
+          gfx942: env_scripts/llama3_8x7b.yml
+        doc_options: ["single-node", "multi-node"]
@@ -1,21 +1,15 @@
-dockers:
-  MI355X and MI350X:
-    pull_tag: rocm/megatron-lm:v25.9_gfx950
-    docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.9_gfx950/images/sha256-1a198be32f49efd66d0ff82066b44bd99b3e6b04c8e0e9b36b2c481e13bff7b6
-    components: &docker_components
-      ROCm: 7.0.0
-      Primus: aab4234
-      PyTorch: 2.9.0.dev20250821+rocm7.0.0.lw.git125803b7
-      Python: "3.10"
-      Transformer Engine: 2.2.0.dev0+54dd2bdc
-      Flash Attention: 2.8.3
-      hipBLASLt: 911283acd1
-      Triton: 3.4.0+rocm7.0.0.git56765e8c
-      RCCL: 2.26.6
-  MI325X and MI300X:
-    pull_tag: rocm/megatron-lm:v25.9_gfx942
-    docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.9_gfx942/images/sha256-df6ab8f45b4b9ceb100fb24e19b2019a364e351ee3b324dbe54466a1d67f8357
-    components: *docker_components
+docker:
+  pull_tag: rocm/primus:v26.1
+  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v26.1/images/sha256-4fc8808bdb14117c6af7f38d79c809056e6fdbfd530c1fabbb61d097ddaf820d
+  components:
+    ROCm: 7.1.0
+    PyTorch: 2.10.0.dev20251112+rocm7.1
+    Python: "3.10"
+    Transformer Engine: 2.6.0.dev0+f141f34b
+    Flash Attention: 2.8.3
+    hipBLASLt: 34459f66ea
+    Triton: 3.4.0
+    RCCL: 2.27.7
 model_groups:
  - group: Meta Llama
    tag: llama
@@ -0,0 +1,64 @@
+dockers:
+  - pull_tag: rocm/jax-training:maxtext-v25.11
+    docker_hub_url: https://hub.docker.com/layers/rocm/jax-training/maxtext-v25.11/images/sha256-18e4d8f0b8ce7a7422c58046940dd5f32249960449fca09a562b65fb8eb1562a
+    components:
+      ROCm: 7.1.0
+      JAX: 0.7.1
+      Python: 3.12
+      Transformer Engine: 2.4.0.dev0+281042de
+      hipBLASLt: 1.2.x
+model_groups:
+  - group: Meta Llama
+    tag: llama
+    models:
+      - model: Llama 2 7B
+        mad_tag: jax_maxtext_train_llama-2-7b
+        model_repo: Llama-2-7B
+        precision: bf16
+        multinode_training_script: llama2_7b_multinode.sh
+        doc_options: ["single-node", "multi-node"]
+      - model: Llama 2 70B
+        mad_tag: jax_maxtext_train_llama-2-70b
+        model_repo: Llama-2-70B
+        precision: bf16
+        multinode_training_script: llama2_70b_multinode.sh
+        doc_options: ["single-node", "multi-node"]
+      - model: Llama 3 8B (multi-node)
+        mad_tag: jax_maxtext_train_llama-3-8b
+        multinode_training_script: llama3_8b_multinode.sh
+        doc_options: ["multi-node"]
+      - model: Llama 3 70B (multi-node)
+        mad_tag: jax_maxtext_train_llama-3-70b
+        multinode_training_script: llama3_70b_multinode.sh
+        doc_options: ["multi-node"]
+      - model: Llama 3.1 8B
+        mad_tag: jax_maxtext_train_llama-3.1-8b
+        model_repo: Llama-3.1-8B
+        precision: bf16
+        doc_options: ["single-node"]
+      - model: Llama 3.1 70B
+        mad_tag: jax_maxtext_train_llama-3.1-70b
+        model_repo: Llama-3.1-70B
+        precision: bf16
+        doc_options: ["single-node"]
+      - model: Llama 3.3 70B
+        mad_tag: jax_maxtext_train_llama-3.3-70b
+        model_repo: Llama-3.3-70B
+        precision: bf16
+        doc_options: ["single-node"]
+  - group: DeepSeek
+    tag: deepseek
+    models:
+      - model: DeepSeek-V2-Lite (16B)
+        mad_tag: jax_maxtext_train_deepseek-v2-lite-16b
+        model_repo: DeepSeek-V2-lite
+        precision: bf16
+        doc_options: ["single-node"]
+  - group: Mistral AI
+    tag: mistral
+    models:
+      - model: Mixtral 8x7B
+        mad_tag: jax_maxtext_train_mixtral-8x7b
+        model_repo: Mixtral-8x7B
+        precision: bf16
+        doc_options: ["single-node"]
@@ -0,0 +1,64 @@
+dockers:
+  - pull_tag: rocm/jax-training:maxtext-v25.9.1
+    docker_hub_url: https://hub.docker.com/layers/rocm/jax-training/maxtext-v25.9.1/images/sha256-60946cfbd470f6ee361fc9da740233a4fb2e892727f01719145b1f7627a1cff6
+    components:
+      ROCm: 7.0.0
+      JAX: 0.6.2
+      Python: 3.10.18
+      Transformer Engine: 2.2.0.dev0+c91bac54
+      hipBLASLt: 1.x.x
+model_groups:
+  - group: Meta Llama
+    tag: llama
+    models:
+      - model: Llama 2 7B
+        mad_tag: jax_maxtext_train_llama-2-7b
+        model_repo: Llama-2-7B
+        precision: bf16
+        multinode_training_script: llama2_7b_multinode.sh
+        doc_options: ["single-node", "multi-node"]
+      - model: Llama 2 70B
+        mad_tag: jax_maxtext_train_llama-2-70b
+        model_repo: Llama-2-70B
+        precision: bf16
+        multinode_training_script: llama2_70b_multinode.sh
+        doc_options: ["single-node", "multi-node"]
+      - model: Llama 3 8B (multi-node)
+        mad_tag: jax_maxtext_train_llama-3-8b
+        multinode_training_script: llama3_8b_multinode.sh
+        doc_options: ["multi-node"]
+      - model: Llama 3 70B (multi-node)
+        mad_tag: jax_maxtext_train_llama-3-70b
+        multinode_training_script: llama3_70b_multinode.sh
+        doc_options: ["multi-node"]
+      - model: Llama 3.1 8B
+        mad_tag: jax_maxtext_train_llama-3.1-8b
+        model_repo: Llama-3.1-8B
+        precision: bf16
+        doc_options: ["single-node"]
+      - model: Llama 3.1 70B
+        mad_tag: jax_maxtext_train_llama-3.1-70b
+        model_repo: Llama-3.1-70B
+        precision: bf16
+        doc_options: ["single-node"]
+      - model: Llama 3.3 70B
+        mad_tag: jax_maxtext_train_llama-3.3-70b
+        model_repo: Llama-3.3-70B
+        precision: bf16
+        doc_options: ["single-node"]
+  - group: DeepSeek
+    tag: deepseek
+    models:
+      - model: DeepSeek-V2-Lite (16B)
+        mad_tag: jax_maxtext_train_deepseek-v2-lite-16b
+        model_repo: DeepSeek-V2-lite
+        precision: bf16
+        doc_options: ["single-node"]
+  - group: Mistral AI
+    tag: mistral
+    models:
+      - model: Mixtral 8x7B
+        mad_tag: jax_maxtext_train_mixtral-8x7b
+        model_repo: Mixtral-8x7B
+        precision: bf16
+        doc_options: ["single-node"]
@@ -0,0 +1,88 @@
+dockers:
+  - pull_tag: rocm/jax-training:maxtext-v26.1
+    docker_hub_url: https://hub.docker.com/layers/rocm/jax-training/maxtext-v26.1/images/sha256-901083bde353fe6362ada3036e452c792b2c96124e5900f4e9b5946c02ff9d6a
+    components:
+      ROCm: 7.1.1
+      JAX: 0.8.2
+      Python: 3.12
+      Transformer Engine: 2.8.0.dev0+aec00a7f
+      hipBLASLt: 1.2.x
+model_groups:
+  - group: Meta Llama
+    tag: llama
+    models:
+      - model: Llama 2 7B
+        mad_tag: jax_maxtext_train_llama-2-7b
+        model_repo: Llama-2-7B
+        precision: bf16
+        multinode_config:
+          gfx950: env_scripts/gfx950_llama2_7b.yml
+          gfx942: env_scripts/llama2_7b.yml
+        doc_options: ["single-node", "multi-node"]
+      - model: Llama 2 70B
+        mad_tag: jax_maxtext_train_llama-2-70b
+        model_repo: Llama-2-70B
+        precision: bf16
+        multinode_config:
+          gfx950: env_scripts/gfx950_llama2_70b.yml
+          gfx942: env_scripts/llama2_70b.yml
+        doc_options: ["single-node", "multi-node"]
+      - model: Llama 3 8B (multi-node)
+        mad_tag: jax_maxtext_train_llama-3-8b
+        multinode_config:
+          gfx950: env_scripts/gfx950_llama3_8b.yml
+          gfx942: env_scripts/llama3_8b.yml
+        doc_options: ["multi-node"]
+      - model: Llama 3 70B (multi-node)
+        mad_tag: jax_maxtext_train_llama-3-70b
+        multinode_config:
+          gfx950: env_scripts/gfx950_llama3_70b.yml
+          gfx942: env_scripts/llama3_70b.yml
+        doc_options: ["multi-node"]
+      - model: Llama 3.1 8B
+        mad_tag: jax_maxtext_train_llama-3.1-8b
+        model_repo: Llama-3.1-8B
+        precision: bf16
+        doc_options: ["single-node"]
+      - model: Llama 3.1 70B
+        mad_tag: jax_maxtext_train_llama-3.1-70b
+        model_repo: Llama-3.1-70B
+        precision: bf16
+        doc_options: ["single-node"]
+      - model: Llama 3.1 405B (multi-node)
+        mad_tag: jax_maxtext_train_llama-3.1-405b
+        model_repo: Llama-3.1-405B
+        precision: bf16
+        multinode_config:
+          gfx950: env_scripts/gfx950_llama3_405b.yml
+        doc_options: ["multi-node"]
+      - model: Llama 3.3 70B
+        mad_tag: jax_maxtext_train_llama-3.3-70b
+        model_repo: Llama-3.3-70B
+        precision: bf16
+        multinode_config:
+          gfx950: env_scripts/gfx950_llama3.3_70b.yml
+          gfx942: env_scripts/llama3.3_70b.yml
+        doc_options: ["single-node", "multi-node"]
+  - group: DeepSeek
+    tag: deepseek
+    models:
+      - model: DeepSeek-V2-Lite (16B)
+        mad_tag: jax_maxtext_train_deepseek-v2-lite-16b
+        model_repo: DeepSeek-V2-lite
+        precision: bf16
+        multinode_config:
+          gfx950: env_scripts/gfx950_deepseek2_16b.yml
+          gfx942: env_scripts/deepseek2_16b.yml
+        doc_options: ["single-node", "multi-node"]
+  - group: Mistral AI
+    tag: mistral
+    models:
+      - model: Mixtral 8x7B
+        mad_tag: jax_maxtext_train_mixtral-8x7b
+        model_repo: Mixtral-8x7B
+        precision: bf16
+        multinode_config:
+          gfx950: env_scripts/gfx950_mixtral_8x7b.yml
+          gfx942: env_scripts/llama3_8x7b.yml
+        doc_options: ["single-node", "multi-node"]
@@ -0,0 +1,49 @@
+docker:
+  pull_tag: rocm/primus:v25.10
+  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.10/images/sha256-140c37cd2eeeb183759b9622543fc03cc210dc97cbfa18eeefdcbda84420c197
+  components:
+    ROCm: 7.1.0
+    Primus: 0.3.0
+    Primus Turbo: 0.1.1
+    PyTorch: 2.10.0.dev20251112+rocm7.1
+    Python: "3.10"
+    Transformer Engine: 2.4.0.dev0+32e2d1d4
+    Flash Attention: 2.8.3
+    hipBLASLt: 1.2.0-09ab7153e2
+    Triton: 3.4.0
+    RCCL: 2.27.7
+model_groups:
+  - group: Meta Llama
+    tag: llama
+    models:
+      - model: Llama 3.3 70B
+        mad_tag: pyt_megatron_lm_train_llama-3.3-70b
+      - model: Llama 3.1 8B
+        mad_tag: pyt_megatron_lm_train_llama-3.1-8b
+      - model: Llama 3.1 70B
+        mad_tag: pyt_megatron_lm_train_llama-3.1-70b
+      - model: Llama 2 7B
+        mad_tag: pyt_megatron_lm_train_llama-2-7b
+      - model: Llama 2 70B
+        mad_tag: pyt_megatron_lm_train_llama-2-70b
+  - group: DeepSeek
+    tag: deepseek
+    models:
+      - model: DeepSeek-V3 (proxy)
+        mad_tag: pyt_megatron_lm_train_deepseek-v3-proxy
+      - model: DeepSeek-V2-Lite
+        mad_tag: pyt_megatron_lm_train_deepseek-v2-lite-16b
+  - group: Mistral AI
+    tag: mistral
+    models:
+      - model: Mixtral 8x7B
+        mad_tag: pyt_megatron_lm_train_mixtral-8x7b
+      - model: Mixtral 8x22B (proxy)
+        mad_tag: pyt_megatron_lm_train_mixtral-8x22b-proxy
+  - group: Qwen
+    tag: qwen
+    models:
+      - model: Qwen 2.5 7B
+        mad_tag: pyt_megatron_lm_train_qwen2.5-7b
+      - model: Qwen 2.5 72B
+        mad_tag: pyt_megatron_lm_train_qwen2.5-72b
@@ -0,0 +1,47 @@
+docker:
+  pull_tag: rocm/primus:v25.11
+  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.11/images/sha256-71aa65a9bfc8e9dd18bce5b68c81caff864f223e9afa75dc1b719671a1f4a3c3
+  components:
+    ROCm: 7.1.0
+    PyTorch: 2.10.0.dev20251112+rocm7.1
+    Python: "3.10"
+    Transformer Engine: 2.4.0.dev0+32e2d1d4
+    Flash Attention: 2.8.3
+    hipBLASLt: 1.2.0-09ab7153e2
+    Triton: 3.4.0
+    RCCL: 2.27.7
+model_groups:
+  - group: Meta Llama
+    tag: llama
+    models:
+      - model: Llama 3.3 70B
+        mad_tag: pyt_megatron_lm_train_llama-3.3-70b
+      - model: Llama 3.1 8B
+        mad_tag: pyt_megatron_lm_train_llama-3.1-8b
+      - model: Llama 3.1 70B
+        mad_tag: pyt_megatron_lm_train_llama-3.1-70b
+      - model: Llama 2 7B
+        mad_tag: pyt_megatron_lm_train_llama-2-7b
+      - model: Llama 2 70B
+        mad_tag: pyt_megatron_lm_train_llama-2-70b
+  - group: DeepSeek
+    tag: deepseek
+    models:
+      - model: DeepSeek-V3 (proxy)
+        mad_tag: pyt_megatron_lm_train_deepseek-v3-proxy
+      - model: DeepSeek-V2-Lite
+        mad_tag: pyt_megatron_lm_train_deepseek-v2-lite-16b
+  - group: Mistral AI
+    tag: mistral
+    models:
+      - model: Mixtral 8x7B
+        mad_tag: pyt_megatron_lm_train_mixtral-8x7b
+      - model: Mixtral 8x22B (proxy)
+        mad_tag: pyt_megatron_lm_train_mixtral-8x22b-proxy
+  - group: Qwen
+    tag: qwen
+    models:
+      - model: Qwen 2.5 7B
+        mad_tag: pyt_megatron_lm_train_qwen2.5-7b
+      - model: Qwen 2.5 72B
+        mad_tag: pyt_megatron_lm_train_qwen2.5-72b
@@ -0,0 +1,53 @@
+dockers:
+  MI355X and MI350X:
+    pull_tag: rocm/megatron-lm:v25.9_gfx950
+    docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.9_gfx950/images/sha256-1a198be32f49efd66d0ff82066b44bd99b3e6b04c8e0e9b36b2c481e13bff7b6
+    components: &docker_components
+      ROCm: 7.0.0
+      Primus: aab4234
+      PyTorch: 2.9.0.dev20250821+rocm7.0.0.lw.git125803b7
+      Python: "3.10"
+      Transformer Engine: 2.2.0.dev0+54dd2bdc
+      Flash Attention: 2.8.3
+      hipBLASLt: 911283acd1
+      Triton: 3.4.0+rocm7.0.0.git56765e8c
+      RCCL: 2.26.6
+  MI325X and MI300X:
+    pull_tag: rocm/megatron-lm:v25.9_gfx942
+    docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.9_gfx942/images/sha256-df6ab8f45b4b9ceb100fb24e19b2019a364e351ee3b324dbe54466a1d67f8357
+    components: *docker_components
+model_groups:
+  - group: Meta Llama
+    tag: llama
+    models:
+      - model: Llama 3.3 70B
+        mad_tag: pyt_megatron_lm_train_llama-3.3-70b
+      - model: Llama 3.1 8B
+        mad_tag: pyt_megatron_lm_train_llama-3.1-8b
+      - model: Llama 3.1 70B
+        mad_tag: pyt_megatron_lm_train_llama-3.1-70b
+      - model: Llama 2 7B
+        mad_tag: pyt_megatron_lm_train_llama-2-7b
+      - model: Llama 2 70B
+        mad_tag: pyt_megatron_lm_train_llama-2-70b
+  - group: DeepSeek
+    tag: deepseek
+    models:
+      - model: DeepSeek-V3 (proxy)
+        mad_tag: pyt_megatron_lm_train_deepseek-v3-proxy
+      - model: DeepSeek-V2-Lite
+        mad_tag: pyt_megatron_lm_train_deepseek-v2-lite-16b
+  - group: Mistral AI
+    tag: mistral
+    models:
+      - model: Mixtral 8x7B
+        mad_tag: pyt_megatron_lm_train_mixtral-8x7b
+      - model: Mixtral 8x22B (proxy)
+        mad_tag: pyt_megatron_lm_train_mixtral-8x22b-proxy
+  - group: Qwen
+    tag: qwen
+    models:
+      - model: Qwen 2.5 7B
+        mad_tag: pyt_megatron_lm_train_qwen2.5-7b
+      - model: Qwen 2.5 72B
+        mad_tag: pyt_megatron_lm_train_qwen2.5-72b
@@ -0,0 +1,58 @@
+docker:
+  pull_tag: rocm/primus:v25.10
+  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.10/images/sha256-140c37cd2eeeb183759b9622543fc03cc210dc97cbfa18eeefdcbda84420c197
+  components:
+    ROCm: 7.1.0
+    PyTorch: 2.10.0.dev20251112+rocm7.1
+    Python: "3.10"
+    Transformer Engine: 2.4.0.dev0+32e2d1d4
+    Flash Attention: 2.8.3
+    hipBLASLt: 1.2.0-09ab7153e2
+    Triton: 3.4.0
+    RCCL: 2.27.7
+model_groups:
+  - group: Meta Llama
+    tag: llama
+    models:
+      - model: Llama 3.3 70B
+        mad_tag: primus_pyt_megatron_lm_train_llama-3.3-70b
+        config_name: llama3.3_70B-pretrain.yaml
+      - model: Llama 3.1 70B
+        mad_tag: primus_pyt_megatron_lm_train_llama-3.1-70b
+        config_name: llama3.1_70B-pretrain.yaml
+      - model: Llama 3.1 8B
+        mad_tag: primus_pyt_megatron_lm_train_llama-3.1-8b
+        config_name: llama3.1_8B-pretrain.yaml
+      - model: Llama 2 7B
+        mad_tag: primus_pyt_megatron_lm_train_llama-2-7b
+        config_name: llama2_7B-pretrain.yaml
+      - model: Llama 2 70B
+        mad_tag: primus_pyt_megatron_lm_train_llama-2-70b
+        config_name: llama2_70B-pretrain.yaml
+  - group: DeepSeek
+    tag: deepseek
+    models:
+      - model: DeepSeek-V3 (proxy)
+        mad_tag: primus_pyt_megatron_lm_train_deepseek-v3-proxy
+        config_name: deepseek_v3-pretrain.yaml
+      - model: DeepSeek-V2-Lite
+        mad_tag: primus_pyt_megatron_lm_train_deepseek-v2-lite-16b
+        config_name: deepseek_v2_lite-pretrain.yaml
+  - group: Mistral AI
+    tag: mistral
+    models:
+      - model: Mixtral 8x7B
+        mad_tag: primus_pyt_megatron_lm_train_mixtral-8x7b
+        config_name: mixtral_8x7B_v0.1-pretrain.yaml
+      - model: Mixtral 8x22B (proxy)
+        mad_tag: primus_pyt_megatron_lm_train_mixtral-8x22b-proxy
+        config_name: mixtral_8x22B_v0.1-pretrain.yaml
+  - group: Qwen
+    tag: qwen
+    models:
+      - model: Qwen 2.5 7B
+        mad_tag: primus_pyt_megatron_lm_train_qwen2.5-7b
+        config_name: primus_qwen2.5_7B-pretrain.yaml
+      - model: Qwen 2.5 72B
+        mad_tag: primus_pyt_megatron_lm_train_qwen2.5-72b
+        config_name: qwen2.5_72B-pretrain.yaml
@@ -0,0 +1,58 @@
+docker:
+  pull_tag: rocm/primus:v25.11
+  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.11/images/sha256-71aa65a9bfc8e9dd18bce5b68c81caff864f223e9afa75dc1b719671a1f4a3c3
+  components:
+    ROCm: 7.1.0
+    PyTorch: 2.10.0.dev20251112+rocm7.1
+    Python: "3.10"
+    Transformer Engine: 2.4.0.dev0+32e2d1d4
+    Flash Attention: 2.8.3
+    hipBLASLt: 1.2.0-09ab7153e2
+    Triton: 3.4.0
+    RCCL: 2.27.7
+model_groups:
+  - group: Meta Llama
+    tag: llama
+    models:
+      - model: Llama 3.3 70B
+        mad_tag: primus_pyt_megatron_lm_train_llama-3.3-70b
+        config_name: llama3.3_70B-pretrain.yaml
+      - model: Llama 3.1 70B
+        mad_tag: primus_pyt_megatron_lm_train_llama-3.1-70b
+        config_name: llama3.1_70B-pretrain.yaml
+      - model: Llama 3.1 8B
+        mad_tag: primus_pyt_megatron_lm_train_llama-3.1-8b
+        config_name: llama3.1_8B-pretrain.yaml
+      - model: Llama 2 7B
+        mad_tag: primus_pyt_megatron_lm_train_llama-2-7b
+        config_name: llama2_7B-pretrain.yaml
+      - model: Llama 2 70B
+        mad_tag: primus_pyt_megatron_lm_train_llama-2-70b
+        config_name: llama2_70B-pretrain.yaml
+  - group: DeepSeek
+    tag: deepseek
+    models:
+      - model: DeepSeek-V3 (proxy)
+        mad_tag: primus_pyt_megatron_lm_train_deepseek-v3-proxy
+        config_name: deepseek_v3-pretrain.yaml
+      - model: DeepSeek-V2-Lite
+        mad_tag: primus_pyt_megatron_lm_train_deepseek-v2-lite-16b
+        config_name: deepseek_v2_lite-pretrain.yaml
+  - group: Mistral AI
+    tag: mistral
+    models:
+      - model: Mixtral 8x7B
+        mad_tag: primus_pyt_megatron_lm_train_mixtral-8x7b
+        config_name: mixtral_8x7B_v0.1-pretrain.yaml
+      - model: Mixtral 8x22B (proxy)
+        mad_tag: primus_pyt_megatron_lm_train_mixtral-8x22b-proxy
+        config_name: mixtral_8x22B_v0.1-pretrain.yaml
+  - group: Qwen
+    tag: qwen
+    models:
+      - model: Qwen 2.5 7B
+        mad_tag: primus_pyt_megatron_lm_train_qwen2.5-7b
+        config_name: primus_qwen2.5_7B-pretrain.yaml
+      - model: Qwen 2.5 72B
+        mad_tag: primus_pyt_megatron_lm_train_qwen2.5-72b
+        config_name: qwen2.5_72B-pretrain.yaml
@@ -0,0 +1,65 @@
+dockers:
+  MI355X and MI350X:
+    pull_tag: rocm/primus:v25.9_gfx950
+    docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.9_gfx950/images/sha256-1a198be32f49efd66d0ff82066b44bd99b3e6b04c8e0e9b36b2c481e13bff7b6
+    components: &docker_components
+      ROCm: 7.0.0
+      Primus: 0.3.0
+      Primus Turbo: 0.1.1
+      PyTorch: 2.9.0.dev20250821+rocm7.0.0.lw.git125803b7
+      Python: "3.10"
+      Transformer Engine: 2.2.0.dev0+54dd2bdc
+      Flash Attention: 2.8.3
+      hipBLASLt: 911283acd1
+      Triton: 3.4.0+rocm7.0.0.git56765e8c
+      RCCL: 2.26.6
+  MI325X and MI300X:
+    pull_tag: rocm/primus:v25.9_gfx942
+    docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.9_gfx942/images/sha256-df6ab8f45b4b9ceb100fb24e19b2019a364e351ee3b324dbe54466a1d67f8357
+    components: *docker_components
+model_groups:
+  - group: Meta Llama
+    tag: llama
+    models:
+      - model: Llama 3.3 70B
+        mad_tag: primus_pyt_megatron_lm_train_llama-3.3-70b
+        config_name: llama3.3_70B-pretrain.yaml
+      - model: Llama 3.1 70B
+        mad_tag: primus_pyt_megatron_lm_train_llama-3.1-70b
+        config_name: llama3.1_70B-pretrain.yaml
+      - model: Llama 3.1 8B
+        mad_tag: primus_pyt_megatron_lm_train_llama-3.1-8b
+        config_name: llama3.1_8B-pretrain.yaml
+      - model: Llama 2 7B
+        mad_tag: primus_pyt_megatron_lm_train_llama-2-7b
+        config_name: llama2_7B-pretrain.yaml
+      - model: Llama 2 70B
+        mad_tag: primus_pyt_megatron_lm_train_llama-2-70b
+        config_name: llama2_70B-pretrain.yaml
+  - group: DeepSeek
+    tag: deepseek
+    models:
+      - model: DeepSeek-V3 (proxy)
+        mad_tag: primus_pyt_megatron_lm_train_deepseek-v3-proxy
+        config_name: deepseek_v3-pretrain.yaml
+      - model: DeepSeek-V2-Lite
+        mad_tag: primus_pyt_megatron_lm_train_deepseek-v2-lite-16b
+        config_name: deepseek_v2_lite-pretrain.yaml
+  - group: Mistral AI
+    tag: mistral
+    models:
+      - model: Mixtral 8x7B
+        mad_tag: primus_pyt_megatron_lm_train_mixtral-8x7b
+        config_name: mixtral_8x7B_v0.1-pretrain.yaml
+      - model: Mixtral 8x22B (proxy)
+        mad_tag: primus_pyt_megatron_lm_train_mixtral-8x22b-proxy
+        config_name: mixtral_8x22B_v0.1-pretrain.yaml
+  - group: Qwen
+    tag: qwen
+    models:
+      - model: Qwen 2.5 7B
+        mad_tag: primus_pyt_megatron_lm_train_qwen2.5-7b
+        config_name: primus_qwen2.5_7B-pretrain.yaml
+      - model: Qwen 2.5 72B
+        mad_tag: primus_pyt_megatron_lm_train_qwen2.5-72b
+        config_name: qwen2.5_72B-pretrain.yaml
@@ -0,0 +1,32 @@
+docker:
+  pull_tag: rocm/primus:v25.10
+  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.10/images/sha256-140c37cd2eeeb183759b9622543fc03cc210dc97cbfa18eeefdcbda84420c197
+  components:
+    ROCm: 7.1.0
+    PyTorch: 2.10.0.dev20251112+rocm7.1
+    Python: "3.10"
+    Transformer Engine: 2.4.0.dev0+32e2d1d4
+    Flash Attention: 2.8.3
+    hipBLASLt: 1.2.0-09ab7153e2
+model_groups:
+  - group: Meta Llama
+    tag: llama
+    models:
+      - model: Llama 3.1 8B
+        mad_tag: primus_pyt_train_llama-3.1-8b
+        model_repo: Llama-3.1-8B
+        url: https://huggingface.co/meta-llama/Llama-3.1-8B
+        precision: BF16
+      - model: Llama 3.1 70B
+        mad_tag: primus_pyt_train_llama-3.1-70b
+        model_repo: Llama-3.1-70B
+        url: https://huggingface.co/meta-llama/Llama-3.1-70B
+        precision: BF16
+  - group: DeepSeek
+    tag: deepseek
+    models:
+      - model: DeepSeek V2 16B
+        mad_tag: primus_pyt_train_deepseek-v2
+        model_repo: DeepSeek-V2
+        url: https://huggingface.co/deepseek-ai/DeepSeek-V2
+        precision: BF16
@@ -0,0 +1,32 @@
+docker:
+  pull_tag: rocm/primus:v25.11
+  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.11/images/sha256-71aa65a9bfc8e9dd18bce5b68c81caff864f223e9afa75dc1b719671a1f4a3c3
+  components:
+    ROCm: 7.1.0
+    PyTorch: 2.10.0.dev20251112+rocm7.1
+    Python: "3.10"
+    Transformer Engine: 2.4.0.dev0+32e2d1d4
+    Flash Attention: 2.8.3
+    hipBLASLt: 1.2.0-09ab7153e2
+model_groups:
+  - group: Meta Llama
+    tag: llama
+    models:
+      - model: Llama 3.1 8B
+        mad_tag: primus_pyt_train_llama-3.1-8b
+        model_repo: Llama-3.1-8B
+        url: https://huggingface.co/meta-llama/Llama-3.1-8B
+        precision: BF16
+      - model: Llama 3.1 70B
+        mad_tag: primus_pyt_train_llama-3.1-70b
+        model_repo: Llama-3.1-70B
+        url: https://huggingface.co/meta-llama/Llama-3.1-70B
+        precision: BF16
+  - group: DeepSeek
+    tag: deepseek
+    models:
+      - model: DeepSeek V3 16B
+        mad_tag: primus_pyt_train_deepseek-v3-16b
+        model_repo: DeepSeek-V3
+        url: https://huggingface.co/deepseek-ai/DeepSeek-V3
+        precision: BF16
@@ -0,0 +1,39 @@
+dockers:
+  MI355X and MI350X:
+    pull_tag: rocm/primus:v25.9_gfx950
+    docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.9_gfx950/images/sha256-1a198be32f49efd66d0ff82066b44bd99b3e6b04c8e0e9b36b2c481e13bff7b6
+    components: &docker_components
+      ROCm: 7.0.0
+      Primus: 0.3.0
+      Primus Turbo: 0.1.1
+      PyTorch: 2.9.0.dev20250821+rocm7.0.0.lw.git125803b7
+      Python: "3.10"
+      Transformer Engine: 2.2.0.dev0+54dd2bdc
+      Flash Attention: 2.8.3
+      hipBLASLt: 911283acd1
+      Triton: 3.4.0+rocm7.0.0.git56765e8c
+      RCCL: 2.26.6
+  MI325X and MI300X:
+    pull_tag: rocm/primus:v25.9_gfx942
+    docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.9_gfx942/images/sha256-df6ab8f45b4b9ceb100fb24e19b2019a364e351ee3b324dbe54466a1d67f8357
+    components: *docker_components
+model_groups:
+  - group: Meta Llama
+    tag: llama
+    models:
+    - model: Llama 3.1 8B
+      mad_tag: primus_pyt_train_llama-3.1-8b
+      model_repo: meta-llama/Llama-3.1-8B
+      url: https://huggingface.co/meta-llama/Llama-3.1-8B
+      precision: BF16
+      config_file:
+        bf16: "./llama3_8b_fsdp_bf16.toml"
+        fp8: "./llama3_8b_fsdp_fp8.toml"
+    - model: Llama 3.1 70B
+      mad_tag: primus_pyt_train_llama-3.1-70b
+      model_repo: meta-llama/Llama-3.1-70B
+      url: https://huggingface.co/meta-llama/Llama-3.1-70B
+      precision: BF16
+      config_file:
+        bf16: "./llama3_70b_fsdp_bf16.toml"
+        fp8: "./llama3_70b_fsdp_fp8.toml"
@@ -0,0 +1,197 @@
+docker:
+  pull_tag: rocm/primus:v25.10
+  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.10/images/sha256-140c37cd2eeeb183759b9622543fc03cc210dc97cbfa18eeefdcbda84420c197
+  components:
+    ROCm: 7.1.0
+    Primus: 0.3.0
+    Primus Turbo: 0.1.1
+    PyTorch: 2.10.0.dev20251112+rocm7.1
+    Python: "3.10"
+    Transformer Engine: 2.4.0.dev0+32e2d1d4
+    Flash Attention: 2.8.3
+    hipBLASLt: 1.2.0-09ab7153e2
+model_groups:
+  - group: Meta Llama
+    tag: llama
+    models:
+    - model: Llama 4 Scout 17B-16E
+      mad_tag: pyt_train_llama-4-scout-17b-16e
+      model_repo: Llama-4-17B_16E
+      url: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 3.3 70B
+      mad_tag: pyt_train_llama-3.3-70b
+      model_repo: Llama-3.3-70B
+      url: https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora, finetune_qlora]
+    - model: Llama 3.2 1B
+      mad_tag: pyt_train_llama-3.2-1b
+      model_repo: Llama-3.2-1B
+      url: https://huggingface.co/meta-llama/Llama-3.2-1B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 3.2 3B
+      mad_tag: pyt_train_llama-3.2-3b
+      model_repo: Llama-3.2-3B
+      url: https://huggingface.co/meta-llama/Llama-3.2-3B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 3.2 Vision 11B
+      mad_tag: pyt_train_llama-3.2-vision-11b
+      model_repo: Llama-3.2-Vision-11B
+      url: https://huggingface.co/meta-llama/Llama-3.2-11B-Vision
+      precision: BF16
+      training_modes: [finetune_fw]
+    - model: Llama 3.2 Vision 90B
+      mad_tag: pyt_train_llama-3.2-vision-90b
+      model_repo: Llama-3.2-Vision-90B
+      url: https://huggingface.co/meta-llama/Llama-3.2-90B-Vision
+      precision: BF16
+      training_modes: [finetune_fw]
+    - model: Llama 3.1 8B
+      mad_tag: pyt_train_llama-3.1-8b
+      model_repo: Llama-3.1-8B
+      url: https://huggingface.co/meta-llama/Llama-3.1-8B
+      precision: BF16
+      training_modes: [pretrain, finetune_fw, finetune_lora, HF_pretrain]
+    - model: Llama 3.1 70B
+      mad_tag: pyt_train_llama-3.1-70b
+      model_repo: Llama-3.1-70B
+      url: https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct
+      precision: BF16
+      training_modes: [pretrain, finetune_fw, finetune_lora]
+    - model: Llama 3.1 405B
+      mad_tag: pyt_train_llama-3.1-405b
+      model_repo: Llama-3.1-405B
+      url: https://huggingface.co/meta-llama/Llama-3.1-405B
+      precision: BF16
+      training_modes: [finetune_qlora]
+    - model: Llama 3 8B
+      mad_tag: pyt_train_llama-3-8b
+      model_repo: Llama-3-8B
+      url: https://huggingface.co/meta-llama/Meta-Llama-3-8B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 3 70B
+      mad_tag: pyt_train_llama-3-70b
+      model_repo: Llama-3-70B
+      url: https://huggingface.co/meta-llama/Meta-Llama-3-70B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 2 7B
+      mad_tag: pyt_train_llama-2-7b
+      model_repo: Llama-2-7B
+      url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora, finetune_qlora]
+    - model: Llama 2 13B
+      mad_tag: pyt_train_llama-2-13b
+      model_repo: Llama-2-13B
+      url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 2 70B
+      mad_tag: pyt_train_llama-2-70b
+      model_repo: Llama-2-70B
+      url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
+      precision: BF16
+      training_modes: [finetune_lora, finetune_qlora]
+  - group: OpenAI
+    tag: openai
+    models:
+    - model: GPT OSS 20B
+      mad_tag: pyt_train_gpt_oss_20b
+      model_repo: GPT-OSS-20B
+      url: https://huggingface.co/openai/gpt-oss-20b
+      precision: BF16
+      training_modes: [HF_finetune_lora]
+    - model: GPT OSS 120B
+      mad_tag: pyt_train_gpt_oss_120b
+      model_repo: GPT-OSS-120B
+      url: https://huggingface.co/openai/gpt-oss-120b
+      precision: BF16
+      training_modes: [HF_finetune_lora]
+  - group: DeepSeek
+    tag: deepseek
+    models:
+    - model: DeepSeek V2 16B
+      mad_tag: primus_pyt_train_deepseek-v2
+      model_repo: DeepSeek-V2
+      url: https://huggingface.co/deepseek-ai/DeepSeek-V2
+      precision: BF16
+      training_modes: [pretrain]
+  - group: Qwen
+    tag: qwen
+    models:
+    - model: Qwen 3 8B
+      mad_tag: pyt_train_qwen3-8b
+      model_repo: Qwen3-8B
+      url: https://huggingface.co/Qwen/Qwen3-8B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Qwen 3 32B
+      mad_tag: pyt_train_qwen3-32b
+      model_repo: Qwen3-32
+      url: https://huggingface.co/Qwen/Qwen3-32B
+      precision: BF16
+      training_modes: [finetune_lora]
+    - model: Qwen 2.5 32B
+      mad_tag: pyt_train_qwen2.5-32b
+      model_repo: Qwen2.5-32B
+      url: https://huggingface.co/Qwen/Qwen2.5-32B
+      precision: BF16
+      training_modes: [finetune_lora]
+    - model: Qwen 2.5 72B
+      mad_tag: pyt_train_qwen2.5-72b
+      model_repo: Qwen2.5-72B
+      url: https://huggingface.co/Qwen/Qwen2.5-72B
+      precision: BF16
+      training_modes: [finetune_lora]
+    - model: Qwen 2 1.5B
+      mad_tag: pyt_train_qwen2-1.5b
+      model_repo: Qwen2-1.5B
+      url: https://huggingface.co/Qwen/Qwen2-1.5B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Qwen 2 7B
+      mad_tag: pyt_train_qwen2-7b
+      model_repo: Qwen2-7B
+      url: https://huggingface.co/Qwen/Qwen2-7B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+  - group: Stable Diffusion
+    tag: sd
+    models:
+    - model: Stable Diffusion XL
+      mad_tag: pyt_huggingface_stable_diffusion_xl_2k_lora_finetuning
+      model_repo: SDXL
+      url: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0
+      precision: BF16
+      training_modes: [posttrain]
+  - group: Flux
+    tag: flux
+    models:
+    - model: FLUX.1-dev
+      mad_tag: pyt_train_flux
+      model_repo: Flux
+      url: https://huggingface.co/black-forest-labs/FLUX.1-dev
+      precision: BF16
+      training_modes: [posttrain]
+  - group: NCF
+    tag: ncf
+    models:
+    - model: NCF
+      mad_tag: pyt_ncf_training
+      model_repo:
+      url: https://github.com/ROCm/FluxBenchmark
+      precision: FP32
+  - group: DLRM
+    tag: dlrm
+    models:
+    - model: DLRM v2
+      mad_tag: pyt_train_dlrm
+      model_repo: DLRM
+      url: https://github.com/AMD-AGI/DLRMBenchmark
+      training_modes: [pretrain]
@@ -0,0 +1,195 @@
+docker:
+  pull_tag: rocm/primus:v25.11
+  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.11/images/sha256-71aa65a9bfc8e9dd18bce5b68c81caff864f223e9afa75dc1b719671a1f4a3c3
+  components:
+    ROCm: 7.1.0
+    PyTorch: 2.10.0.dev20251112+rocm7.1
+    Python: "3.10"
+    Transformer Engine: 2.4.0.dev0+32e2d1d4
+    Flash Attention: 2.8.3
+    hipBLASLt: 1.2.0-09ab7153e2
+model_groups:
+  - group: Meta Llama
+    tag: llama
+    models:
+    - model: Llama 4 Scout 17B-16E
+      mad_tag: pyt_train_llama-4-scout-17b-16e
+      model_repo: Llama-4-17B_16E
+      url: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 3.3 70B
+      mad_tag: pyt_train_llama-3.3-70b
+      model_repo: Llama-3.3-70B
+      url: https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora, finetune_qlora]
+    - model: Llama 3.2 1B
+      mad_tag: pyt_train_llama-3.2-1b
+      model_repo: Llama-3.2-1B
+      url: https://huggingface.co/meta-llama/Llama-3.2-1B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 3.2 3B
+      mad_tag: pyt_train_llama-3.2-3b
+      model_repo: Llama-3.2-3B
+      url: https://huggingface.co/meta-llama/Llama-3.2-3B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 3.2 Vision 11B
+      mad_tag: pyt_train_llama-3.2-vision-11b
+      model_repo: Llama-3.2-Vision-11B
+      url: https://huggingface.co/meta-llama/Llama-3.2-11B-Vision
+      precision: BF16
+      training_modes: [finetune_fw]
+    - model: Llama 3.2 Vision 90B
+      mad_tag: pyt_train_llama-3.2-vision-90b
+      model_repo: Llama-3.2-Vision-90B
+      url: https://huggingface.co/meta-llama/Llama-3.2-90B-Vision
+      precision: BF16
+      training_modes: [finetune_fw]
+    - model: Llama 3.1 8B
+      mad_tag: pyt_train_llama-3.1-8b
+      model_repo: Llama-3.1-8B
+      url: https://huggingface.co/meta-llama/Llama-3.1-8B
+      precision: BF16
+      training_modes: [pretrain, finetune_fw, finetune_lora, HF_pretrain]
+    - model: Llama 3.1 70B
+      mad_tag: pyt_train_llama-3.1-70b
+      model_repo: Llama-3.1-70B
+      url: https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct
+      precision: BF16
+      training_modes: [pretrain, finetune_fw, finetune_lora]
+    - model: Llama 3.1 405B
+      mad_tag: pyt_train_llama-3.1-405b
+      model_repo: Llama-3.1-405B
+      url: https://huggingface.co/meta-llama/Llama-3.1-405B
+      precision: BF16
+      training_modes: [finetune_qlora]
+    - model: Llama 3 8B
+      mad_tag: pyt_train_llama-3-8b
+      model_repo: Llama-3-8B
+      url: https://huggingface.co/meta-llama/Meta-Llama-3-8B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 3 70B
+      mad_tag: pyt_train_llama-3-70b
+      model_repo: Llama-3-70B
+      url: https://huggingface.co/meta-llama/Meta-Llama-3-70B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 2 7B
+      mad_tag: pyt_train_llama-2-7b
+      model_repo: Llama-2-7B
+      url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora, finetune_qlora]
+    - model: Llama 2 13B
+      mad_tag: pyt_train_llama-2-13b
+      model_repo: Llama-2-13B
+      url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 2 70B
+      mad_tag: pyt_train_llama-2-70b
+      model_repo: Llama-2-70B
+      url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
+      precision: BF16
+      training_modes: [finetune_lora, finetune_qlora]
+  - group: OpenAI
+    tag: openai
+    models:
+    - model: GPT OSS 20B
+      mad_tag: pyt_train_gpt_oss_20b
+      model_repo: GPT-OSS-20B
+      url: https://huggingface.co/openai/gpt-oss-20b
+      precision: BF16
+      training_modes: [HF_finetune_lora]
+    - model: GPT OSS 120B
+      mad_tag: pyt_train_gpt_oss_120b
+      model_repo: GPT-OSS-120B
+      url: https://huggingface.co/openai/gpt-oss-120b
+      precision: BF16
+      training_modes: [HF_finetune_lora]
+  - group: DeepSeek
+    tag: deepseek
+    models:
+    - model: DeepSeek V2 16B
+      mad_tag: primus_pyt_train_deepseek-v2
+      model_repo: DeepSeek-V2
+      url: https://huggingface.co/deepseek-ai/DeepSeek-V2
+      precision: BF16
+      training_modes: [pretrain]
+  - group: Qwen
+    tag: qwen
+    models:
+    - model: Qwen 3 8B
+      mad_tag: pyt_train_qwen3-8b
+      model_repo: Qwen3-8B
+      url: https://huggingface.co/Qwen/Qwen3-8B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Qwen 3 32B
+      mad_tag: pyt_train_qwen3-32b
+      model_repo: Qwen3-32
+      url: https://huggingface.co/Qwen/Qwen3-32B
+      precision: BF16
+      training_modes: [finetune_lora]
+    - model: Qwen 2.5 32B
+      mad_tag: pyt_train_qwen2.5-32b
+      model_repo: Qwen2.5-32B
+      url: https://huggingface.co/Qwen/Qwen2.5-32B
+      precision: BF16
+      training_modes: [finetune_lora]
+    - model: Qwen 2.5 72B
+      mad_tag: pyt_train_qwen2.5-72b
+      model_repo: Qwen2.5-72B
+      url: https://huggingface.co/Qwen/Qwen2.5-72B
+      precision: BF16
+      training_modes: [finetune_lora]
+    - model: Qwen 2 1.5B
+      mad_tag: pyt_train_qwen2-1.5b
+      model_repo: Qwen2-1.5B
+      url: https://huggingface.co/Qwen/Qwen2-1.5B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Qwen 2 7B
+      mad_tag: pyt_train_qwen2-7b
+      model_repo: Qwen2-7B
+      url: https://huggingface.co/Qwen/Qwen2-7B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+  - group: Stable Diffusion
+    tag: sd
+    models:
+    - model: Stable Diffusion XL
+      mad_tag: pyt_huggingface_stable_diffusion_xl_2k_lora_finetuning
+      model_repo: SDXL
+      url: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0
+      precision: BF16
+      training_modes: [posttrain]
+  - group: Flux
+    tag: flux
+    models:
+    - model: FLUX.1-dev
+      mad_tag: pyt_train_flux
+      model_repo: Flux
+      url: https://huggingface.co/black-forest-labs/FLUX.1-dev
+      precision: BF16
+      training_modes: [posttrain]
+  - group: NCF
+    tag: ncf
+    models:
+    - model: NCF
+      mad_tag: pyt_ncf_training
+      model_repo:
+      url: https://github.com/ROCm/FluxBenchmark
+      precision: FP32
+  - group: DLRM
+    tag: dlrm
+    models:
+    - model: DLRM v2
+      mad_tag: pyt_train_dlrm
+      model_repo: DLRM
+      url: https://github.com/AMD-AGI/DLRMBenchmark
+      training_modes: [pretrain]
@@ -0,0 +1,186 @@
+dockers:
+  MI355X and MI350X:
+    pull_tag: rocm/pytorch-training:v25.9_gfx950
+    docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-training/v25.9_gfx950/images/sha256-1a198be32f49efd66d0ff82066b44bd99b3e6b04c8e0e9b36b2c481e13bff7b6
+    components: &docker_components
+      ROCm: 7.0.0
+      Primus: aab4234
+      PyTorch: 2.9.0.dev20250821+rocm7.0.0.lw.git125803b7
+      Python: "3.10"
+      Transformer Engine: 2.2.0.dev0+54dd2bdc
+      Flash Attention: 2.8.3
+      hipBLASLt: 911283acd1
+      Triton: 3.4.0+rocm7.0.0.git56765e8c
+      RCCL: 2.26.6
+  MI325X and MI300X:
+    pull_tag: rocm/pytorch-training:v25.9_gfx942
+    docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-training/v25.9_gfx942/images/sha256-df6ab8f45b4b9ceb100fb24e19b2019a364e351ee3b324dbe54466a1d67f8357
+    components: *docker_components
+model_groups:
+  - group: Meta Llama
+    tag: llama
+    models:
+    - model: Llama 4 Scout 17B-16E
+      mad_tag: pyt_train_llama-4-scout-17b-16e
+      model_repo: Llama-4-17B_16E
+      url: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 3.3 70B
+      mad_tag: pyt_train_llama-3.3-70b
+      model_repo: Llama-3.3-70B
+      url: https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora, finetune_qlora]
+    - model: Llama 3.2 1B
+      mad_tag: pyt_train_llama-3.2-1b
+      model_repo: Llama-3.2-1B
+      url: https://huggingface.co/meta-llama/Llama-3.2-1B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 3.2 3B
+      mad_tag: pyt_train_llama-3.2-3b
+      model_repo: Llama-3.2-3B
+      url: https://huggingface.co/meta-llama/Llama-3.2-3B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 3.2 Vision 11B
+      mad_tag: pyt_train_llama-3.2-vision-11b
+      model_repo: Llama-3.2-Vision-11B
+      url: https://huggingface.co/meta-llama/Llama-3.2-11B-Vision
+      precision: BF16
+      training_modes: [finetune_fw]
+    - model: Llama 3.2 Vision 90B
+      mad_tag: pyt_train_llama-3.2-vision-90b
+      model_repo: Llama-3.2-Vision-90B
+      url: https://huggingface.co/meta-llama/Llama-3.2-90B-Vision
+      precision: BF16
+      training_modes: [finetune_fw]
+    - model: Llama 3.1 8B
+      mad_tag: pyt_train_llama-3.1-8b
+      model_repo: Llama-3.1-8B
+      url: https://huggingface.co/meta-llama/Llama-3.1-8B
+      precision: BF16
+      training_modes: [pretrain, finetune_fw, finetune_lora, HF_pretrain]
+    - model: Llama 3.1 70B
+      mad_tag: pyt_train_llama-3.1-70b
+      model_repo: Llama-3.1-70B
+      url: https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct
+      precision: BF16
+      training_modes: [pretrain, finetune_fw, finetune_lora]
+    - model: Llama 3.1 405B
+      mad_tag: pyt_train_llama-3.1-405b
+      model_repo: Llama-3.1-405B
+      url: https://huggingface.co/meta-llama/Llama-3.1-405B
+      precision: BF16
+      training_modes: [finetune_qlora]
+    - model: Llama 3 8B
+      mad_tag: pyt_train_llama-3-8b
+      model_repo: Llama-3-8B
+      url: https://huggingface.co/meta-llama/Meta-Llama-3-8B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 3 70B
+      mad_tag: pyt_train_llama-3-70b
+      model_repo: Llama-3-70B
+      url: https://huggingface.co/meta-llama/Meta-Llama-3-70B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 2 7B
+      mad_tag: pyt_train_llama-2-7b
+      model_repo: Llama-2-7B
+      url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora, finetune_qlora]
+    - model: Llama 2 13B
+      mad_tag: pyt_train_llama-2-13b
+      model_repo: Llama-2-13B
+      url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 2 70B
+      mad_tag: pyt_train_llama-2-70b
+      model_repo: Llama-2-70B
+      url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
+      precision: BF16
+      training_modes: [finetune_lora, finetune_qlora]
+  - group: OpenAI
+    tag: openai
+    models:
+    - model: GPT OSS 20B
+      mad_tag: pyt_train_gpt_oss_20b
+      model_repo: GPT-OSS-20B
+      url: https://huggingface.co/openai/gpt-oss-20b
+      precision: BF16
+      training_modes: [HF_finetune_lora]
+    - model: GPT OSS 120B
+      mad_tag: pyt_train_gpt_oss_120b
+      model_repo: GPT-OSS-120B
+      url: https://huggingface.co/openai/gpt-oss-120b
+      precision: BF16
+      training_modes: [HF_finetune_lora]
+  - group: Qwen
+    tag: qwen
+    models:
+    - model: Qwen 3 8B
+      mad_tag: pyt_train_qwen3-8b
+      model_repo: Qwen3-8B
+      url: https://huggingface.co/Qwen/Qwen3-8B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Qwen 3 32B
+      mad_tag: pyt_train_qwen3-32b
+      model_repo: Qwen3-32
+      url: https://huggingface.co/Qwen/Qwen3-32B
+      precision: BF16
+      training_modes: [finetune_lora]
+    - model: Qwen 2.5 32B
+      mad_tag: pyt_train_qwen2.5-32b
+      model_repo: Qwen2.5-32B
+      url: https://huggingface.co/Qwen/Qwen2.5-32B
+      precision: BF16
+      training_modes: [finetune_lora]
+    - model: Qwen 2.5 72B
+      mad_tag: pyt_train_qwen2.5-72b
+      model_repo: Qwen2.5-72B
+      url: https://huggingface.co/Qwen/Qwen2.5-72B
+      precision: BF16
+      training_modes: [finetune_lora]
+    - model: Qwen 2 1.5B
+      mad_tag: pyt_train_qwen2-1.5b
+      model_repo: Qwen2-1.5B
+      url: https://huggingface.co/Qwen/Qwen2-1.5B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Qwen 2 7B
+      mad_tag: pyt_train_qwen2-7b
+      model_repo: Qwen2-7B
+      url: https://huggingface.co/Qwen/Qwen2-7B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+  - group: Stable Diffusion
+    tag: sd
+    models:
+    - model: Stable Diffusion XL
+      mad_tag: pyt_huggingface_stable_diffusion_xl_2k_lora_finetuning
+      model_repo: SDXL
+      url: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0
+      precision: BF16
+      training_modes: [posttrain-p]
+  - group: Flux
+    tag: flux
+    models:
+    - model: FLUX.1-dev
+      mad_tag: pyt_train_flux
+      model_repo: Flux
+      url: https://huggingface.co/black-forest-labs/FLUX.1-dev
+      precision: BF16
+      training_modes: [posttrain-p]
+  - group: NCF
+    tag: ncf
+    models:
+    - model: NCF
+      mad_tag: pyt_ncf_training
+      model_repo:
+      url: https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Recommendation/NCF
+      precision: FP32
@@ -1,22 +1,15 @@
-dockers:
-  MI355X and MI350X:
-    pull_tag: rocm/primus:v25.9_gfx950
-    docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.9_gfx950/images/sha256-1a198be32f49efd66d0ff82066b44bd99b3e6b04c8e0e9b36b2c481e13bff7b6
-    components: &docker_components
-      ROCm: 7.0.0
-      Primus: 0.3.0
-      Primus Turbo: 0.1.1
-      PyTorch: 2.9.0.dev20250821+rocm7.0.0.lw.git125803b7
-      Python: "3.10"
-      Transformer Engine: 2.2.0.dev0+54dd2bdc
-      Flash Attention: 2.8.3
-      hipBLASLt: 911283acd1
-      Triton: 3.4.0+rocm7.0.0.git56765e8c
-      RCCL: 2.26.6
-  MI325X and MI300X:
-    pull_tag: rocm/primus:v25.9_gfx942
-    docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.9_gfx942/images/sha256-df6ab8f45b4b9ceb100fb24e19b2019a364e351ee3b324dbe54466a1d67f8357
-    components: *docker_components
+docker:
+  pull_tag: rocm/primus:v26.1
+  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v26.1/images/sha256-4fc8808bdb14117c6af7f38d79c809056e6fdbfd530c1fabbb61d097ddaf820d
+  components:
+    ROCm: 7.1.0
+    PyTorch: 2.10.0.dev20251112+rocm7.1
+    Python: "3.10"
+    Transformer Engine: 2.6.0.dev0+f141f34b
+    Flash Attention: 2.8.3
+    hipBLASLt: 34459f66ea
+    Triton: 3.4.0
+    RCCL: 2.27.7
 model_groups:
  - group: Meta Llama
    tag: llama
@@ -1,39 +1,32 @@
-dockers:
-  MI355X and MI350X:
-    pull_tag: rocm/primus:v25.9_gfx950
-    docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.9_gfx950/images/sha256-1a198be32f49efd66d0ff82066b44bd99b3e6b04c8e0e9b36b2c481e13bff7b6
-    components: &docker_components
-      ROCm: 7.0.0
-      Primus: 0.3.0
-      Primus Turbo: 0.1.1
-      PyTorch: 2.9.0.dev20250821+rocm7.0.0.lw.git125803b7
-      Python: "3.10"
-      Transformer Engine: 2.2.0.dev0+54dd2bdc
-      Flash Attention: 2.8.3
-      hipBLASLt: 911283acd1
-      Triton: 3.4.0+rocm7.0.0.git56765e8c
-      RCCL: 2.26.6
-  MI325X and MI300X:
-    pull_tag: rocm/primus:v25.9_gfx942
-    docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.9_gfx942/images/sha256-df6ab8f45b4b9ceb100fb24e19b2019a364e351ee3b324dbe54466a1d67f8357
-    components: *docker_components
+docker:
+  pull_tag: rocm/primus:v26.1
+  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v26.1/images/sha256-4fc8808bdb14117c6af7f38d79c809056e6fdbfd530c1fabbb61d097ddaf820d
+  components:
+    ROCm: 7.1.0
+    PyTorch: 2.10.0.dev20251112+rocm7.1
+    Python: "3.10"
+    Transformer Engine: 2.6.0.dev0+f141f34b
+    Flash Attention: 2.8.3
+    hipBLASLt: 34459f66ea
 model_groups:
  - group: Meta Llama
    tag: llama
    models:
-    - model: Llama 3.1 8B
-      mad_tag: primus_pyt_train_llama-3.1-8b
-      model_repo: meta-llama/Llama-3.1-8B
-      url: https://huggingface.co/meta-llama/Llama-3.1-8B
-      precision: BF16
-      config_file:
-        bf16: "./llama3_8b_fsdp_bf16.toml"
-        fp8: "./llama3_8b_fsdp_fp8.toml"
-    - model: Llama 3.1 70B
-      mad_tag: primus_pyt_train_llama-3.1-70b
-      model_repo: meta-llama/Llama-3.1-70B
-      url: https://huggingface.co/meta-llama/Llama-3.1-70B
-      precision: BF16
-      config_file:
-        bf16: "./llama3_70b_fsdp_bf16.toml"
-        fp8: "./llama3_70b_fsdp_fp8.toml"
+      - model: Llama 3.1 8B
+        mad_tag: primus_pyt_train_llama-3.1-8b
+        model_repo: Llama-3.1-8B
+        url: https://huggingface.co/meta-llama/Llama-3.1-8B
+        precision: BF16
+      - model: Llama 3.1 70B
+        mad_tag: primus_pyt_train_llama-3.1-70b
+        model_repo: Llama-3.1-70B
+        url: https://huggingface.co/meta-llama/Llama-3.1-70B
+        precision: BF16
+  - group: DeepSeek
+    tag: deepseek
+    models:
+      - model: DeepSeek V3 16B
+        mad_tag: primus_pyt_train_deepseek-v3-16b
+        model_repo: DeepSeek-V3
+        url: https://huggingface.co/deepseek-ai/DeepSeek-V3
+        precision: BF16
@@ -1,21 +1,13 @@
-dockers:
-  MI355X and MI350X:
-    pull_tag: rocm/pytorch-training:v25.9_gfx950
-    docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-training/v25.9_gfx950/images/sha256-1a198be32f49efd66d0ff82066b44bd99b3e6b04c8e0e9b36b2c481e13bff7b6
-    components: &docker_components
-      ROCm: 7.0.0
-      Primus: aab4234
-      PyTorch: 2.9.0.dev20250821+rocm7.0.0.lw.git125803b7
-      Python: "3.10"
-      Transformer Engine: 2.2.0.dev0+54dd2bdc
-      Flash Attention: 2.8.3
-      hipBLASLt: 911283acd1
-      Triton: 3.4.0+rocm7.0.0.git56765e8c
-      RCCL: 2.26.6
-  MI325X and MI300X:
-    pull_tag: rocm/pytorch-training:v25.9_gfx942
-    docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-training/v25.9_gfx942/images/sha256-df6ab8f45b4b9ceb100fb24e19b2019a364e351ee3b324dbe54466a1d67f8357
-    components: *docker_components
+docker:
+  pull_tag: rocm/primus:v26.1
+  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v26.1/images/sha256-4fc8808bdb14117c6af7f38d79c809056e6fdbfd530c1fabbb61d097ddaf820d
+  components:
+    ROCm: 7.1.0
+    PyTorch: 2.10.0.dev20251112+rocm7.1
+    Python: "3.10"
+    Transformer Engine: 2.6.0.dev0+f141f34b
+    Flash Attention: 2.8.3
+    hipBLASLt: 34459f66ea
 model_groups:
  - group: Meta Llama
    tag: llama
@@ -119,6 +111,15 @@ model_groups:
      url: https://huggingface.co/openai/gpt-oss-120b
      precision: BF16
      training_modes: [HF_finetune_lora]
+  - group: DeepSeek
+    tag: deepseek
+    models:
+    - model: DeepSeek V2 16B
+      mad_tag: primus_pyt_train_deepseek-v2
+      model_repo: DeepSeek-V2
+      url: https://huggingface.co/deepseek-ai/DeepSeek-V2
+      precision: BF16
+      training_modes: [pretrain]
  - group: Qwen
    tag: qwen
    models:
@@ -166,7 +167,7 @@ model_groups:
      model_repo: SDXL
      url: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0
      precision: BF16
-      training_modes: [posttrain-p]
+      training_modes: [posttrain]
  - group: Flux
    tag: flux
    models:
@@ -175,12 +176,20 @@ model_groups:
      model_repo: Flux
      url: https://huggingface.co/black-forest-labs/FLUX.1-dev
      precision: BF16
-      training_modes: [posttrain-p]
+      training_modes: [posttrain]
  - group: NCF
    tag: ncf
    models:
    - model: NCF
      mad_tag: pyt_ncf_training
      model_repo:
-      url: https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Recommendation/NCF
+      url: https://github.com/ROCm/FluxBenchmark
      precision: FP32
+  - group: DLRM
+    tag: dlrm
+    models:
+    - model: DLRM v2
+      mad_tag: pyt_train_dlrm
+      model_repo: DLRM
+      url: https://github.com/AMD-AGI/DLRMBenchmark
+      training_modes: [pretrain]
@@ -32,7 +32,7 @@ library_groups:

      - name: "MIGraphX"
        tag: "migraphx"
-        doc_link: "amdmigraphx:reference/cpp"
+        doc_link: "amdmigraphx:reference/MIGraphX-cpp"
        data_types:
          - type: "int8"
            support: "⚠️"
@@ -290,7 +290,7 @@ library_groups:

      - name: "Tensile"
        tag: "tensile"
-        doc_link: "tensile:reference/precision-support"
+        doc_link: "tensile:src/reference/precision-support"
        data_types:
          - type: "int8"
            support: "✅"
@@ -0,0 +1,141 @@
+from docutils import nodes
+from docutils.parsers.rst import Directive
+from docutils.statemachine import ViewList
+from sphinx.util import logging
+from sphinx.util.nodes import nested_parse_with_titles
+import requests
+import re
+
+logger = logging.getLogger(__name__)
+
+class BranchAwareRemoteContent(Directive):
+    """
+    Directive that downloads and includes content from other repositories,
+    matching the branch/tag of the current documentation build.
+
+    Usage:
+    .. remote-content::
+       :repo: owner/repository
+       :path: path/to/file.rst
+       :default_branch: docs/develop  # Branch to use when not on a release
+       :tag_prefix: Docs/  # Optional
+    """
+
+    required_arguments = 0
+    optional_arguments = 0
+    final_argument_whitespace = True
+    has_content = False
+    option_spec = {
+        'repo': str,
+        'path': str,
+        'default_branch': str,  # Branch to use when not on a release tag
+        'start_line': int,      # Include the file from a specific line
+        'tag_prefix': str,      # Prefix for release tags (e.g., 'Docs/')
+    }
+
+    def get_current_version(self):
+        """Get current version/branch being built"""
+        env = self.state.document.settings.env
+        html_context = env.config.html_context
+
+        # Check if building from a tag
+        if "official_branch" in html_context:
+            if html_context["official_branch"] == 0:
+                if "version" in html_context:
+                    # Remove any 'v' prefix
+                    version = html_context["version"]
+                    if re.match(r'^\d+\.\d+\.\d+$', version):
+                        return version
+
+        # Not a version tag, so we'll use the default branch
+        return None
+
+    def get_target_ref(self):
+        """Get target reference for the remote repository"""
+        current_version = self.get_current_version()
+
+        # If it's a version number, use tag prefix and version
+        if current_version:
+            tag_prefix = self.options.get('tag_prefix', '')
+            return f'{tag_prefix}{current_version}'
+
+        # For any other case, use the specified default branch
+        if 'default_branch' not in self.options:
+            logger.warning('No default_branch specified and not building from a version tag')
+            return None
+
+        return self.options['default_branch']
+
+    def construct_raw_url(self, repo, path, ref):
+        """Construct the raw.githubusercontent.com URL"""
+        return f'https://raw.githubusercontent.com/{repo}/{ref}/{path}'
+
+    def fetch_and_parse_content(self, url, source_path):
+        """Fetch content and parse it as RST"""
+        response = requests.get(url)
+        response.raise_for_status()
+        content = response.text
+
+        start_line = self.options.get('start_line', 0)
+
+        # Create ViewList for parsing
+        line_count = 0
+        content_list = ViewList()
+        for line_no, line in enumerate(content.splitlines()):
+            if line_count >= start_line:
+                content_list.append(line, source_path, line_no)
+            line_count+=1 
+
+        # Create a section node and parse content
+        node = nodes.section()
+        nested_parse_with_titles(self.state, content_list, node)
+
+        return node.children
+
+    def run(self):
+        if 'repo' not in self.options or 'path' not in self.options:
+            logger.warning('Both repo and path options are required')
+            return []
+
+        target_ref = self.get_target_ref()
+        if not target_ref:
+            return []
+
+        raw_url = self.construct_raw_url(
+            self.options['repo'],
+            self.options['path'],
+            target_ref
+        )
+
+        try:
+            logger.info(f'Attempting to fetch content from {raw_url}')
+            return self.fetch_and_parse_content(raw_url, self.options['path'])
+        except requests.exceptions.RequestException as e:
+            logger.warning(f'Failed to fetch content from {raw_url}: {str(e)}')
+
+            # If we failed on a tag, try falling back to default_branch
+            if re.match(r'^\d+\.\d+\.\d+$', target_ref) or target_ref.startswith('Docs/'):
+                if 'default_branch' in self.options:
+                    try:
+                        fallback_ref = self.options['default_branch']
+                        logger.info(f'Attempting fallback to {fallback_ref}...')
+
+                        fallback_url = self.construct_raw_url(
+                            self.options['repo'],
+                            self.options['path'],
+                            fallback_ref
+                        )
+
+                        return self.fetch_and_parse_content(fallback_url, self.options['path'])
+                    except requests.exceptions.RequestException as e2:
+                        logger.warning(f'Fallback also failed: {str(e2)}')
+
+            return []
+
+def setup(app):
+    app.add_directive('remote-content', BranchAwareRemoteContent)
+
+    return {
+        'parallel_read_safe': True,
+        'parallel_write_safe': True,
+    }
@@ -19,131 +19,47 @@ The table below summarizes information about ROCm-enabled deep learning framewor
    :widths: 5 3 6 3

    * - Framework
-      - Installation
+      - Installation guide
      - Installation options
      - GitHub

-    * - `PyTorch <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/pytorch-compatibility.html>`__
-      - .. raw:: html
-
-          <a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html"><i class="fas fa-link fa-lg"></i></a>
+    * - :doc:`PyTorch <../compatibility/ml-compatibility/pytorch-compatibility>`
+      - :doc:`link <rocm-install-on-linux:install/3rd-party/pytorch-install>`
      - 
-        - `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html#using-a-docker-image-with-pytorch-pre-installed>`__
-        - `Wheels package <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html#using-a-wheels-package>`__
-        - `ROCm Base Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html#using-the-pytorch-rocm-base-docker-image>`__
-        - `Upstream Docker file <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html#using-the-pytorch-upstream-dockerfile>`__
+        - Docker image
+        - Wheels package
+        - ROCm Base Docker image
+        - Upstream Docker file
      - .. raw:: html

          <a href="https://github.com/ROCm/pytorch"><i class="fab fa-github fa-lg"></i></a>

-    * - `TensorFlow <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/tensorflow-compatibility.html>`__
-      - .. raw:: html
-
-          <a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/tensorflow-install.html"><i class="fas fa-link fa-lg"></i></a>
+    * - :doc:`TensorFlow <../compatibility/ml-compatibility/tensorflow-compatibility>`
+      - :doc:`link <rocm-install-on-linux:install/3rd-party/tensorflow-install>`
      - 
-        - `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/tensorflow-install.html#using-a-docker-image-with-tensorflow-pre-installed>`__
-        - `Wheels package <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/tensorflow-install.html#using-a-wheels-package>`__
+        - Docker image
+        - Wheels package

      - .. raw:: html

          <a href="https://github.com/ROCm/tensorflow-upstream"><i class="fab fa-github fa-lg"></i></a> 

-    * - `JAX <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/jax-compatibility.html>`__
-      - .. raw:: html
-
-          <a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/jax-install.html"><i class="fas fa-link fa-lg"></i></a>
+    * - :doc:`JAX <../compatibility/ml-compatibility/jax-compatibility>`
+      - :doc:`link <rocm-install-on-linux:install/3rd-party/jax-install>`
      - 
-        - `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/jax-install.html#using-a-prebuilt-docker-image>`__
+        - Docker image
      - .. raw:: html

          <a href="https://github.com/ROCm/jax"><i class="fab fa-github fa-lg"></i></a>

-    * - `verl <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/verl-compatibility.html>`__
-      - .. raw:: html
-
-          <a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/verl-install.html"><i class="fas fa-link fa-lg"></i></a>
+    * - :doc:`DGL <../compatibility/ml-compatibility/dgl-compatibility>`
+      - :doc:`link <rocm-install-on-linux:install/3rd-party/dgl-install>`
      - 
-        - `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/verl-install.html#use-a-prebuilt-docker-image-with-verl-pre-installed>`__
-      - .. raw:: html
-
-          <a href="https://github.com/ROCm/verl"><i class="fab fa-github fa-lg"></i></a>
-
-    * - `Stanford Megatron-LM <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/stanford-megatron-lm-compatibility.html>`__
-      - .. raw:: html
-
-          <a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/stanford-megatron-lm-install.html"><i class="fas fa-link fa-lg"></i></a>
-      - 
-        - `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/stanford-megatron-lm-install.html#use-a-prebuilt-docker-image-with-stanford-megatron-lm-pre-installed>`__
-      - .. raw:: html
-
-          <a href="https://github.com/ROCm/Stanford-Megatron-LM"><i class="fab fa-github fa-lg"></i></a>
-
-    * - `DGL <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/dgl-compatibility.html>`__
-      - .. raw:: html
-
-          <a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/dgl-install.html"><i class="fas fa-link fa-lg"></i></a>
-      - 
-        - `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/dgl-install.html#use-a-prebuilt-docker-image-with-dgl-pre-installed>`__
+        - Docker image
      - .. raw:: html

          <a href="https://github.com/ROCm/dgl"><i class="fab fa-github fa-lg"></i></a> 

-    * - `Megablocks <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/megablocks-compatibility.html>`__
-      - .. raw:: html
-
-          <a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/megablocks-install.html"><i class="fas fa-link fa-lg"></i></a>
-      - 
-        - `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/megablocks-install.html#using-a-prebuilt-docker-image-with-megablocks-pre-installed>`__
-      - .. raw:: html
-
-          <a href="https://github.com/ROCm/megablocks"><i class="fab fa-github fa-lg"></i></a>
-
-    * - `Taichi <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/taichi-compatibility.html>`__
-      - .. raw:: html
-
-          <a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/taichi-install.html"><i class="fas fa-link fa-lg"></i></a>
-      - 
-        - `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/taichi-install.html#use-a-prebuilt-docker-image-with-taichi-pre-installed>`__
-        - `Wheels package <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/taichi-install.html#use-a-wheels-package>`__
-
-      - .. raw:: html
-
-          <a href="https://github.com/ROCm/taichi"><i class="fab fa-github fa-lg"></i></a>
-
-    * - `Ray <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/ray-compatibility.html>`__
-      - .. raw:: html
-
-          <a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/ray-install.html"><i class="fas fa-link fa-lg"></i></a>
-      - 
-        - `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/ray-install.html#using-a-prebuilt-docker-image-with-ray-pre-installed>`__
-        - `Wheels package <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/ray-install.html#install-ray-on-bare-metal-or-a-custom-container>`__
-        - `ROCm Base Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/ray-install.html#build-your-own-docker-image>`__
-      - .. raw:: html
-
-          <a href="https://github.com/ROCm/ray"><i class="fab fa-github fa-lg"></i></a>
-
-    * - `llama.cpp <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/llama-cpp-compatibility.html>`__
-      - .. raw:: html
-
-          <a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/llama-cpp-install.html"><i class="fas fa-link fa-lg"></i></a>
-      - 
-        - `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/llama-cpp-install.html#use-a-prebuilt-docker-image-with-llama-cpp-pre-installed>`__
-        - `ROCm Base Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/llama-cpp-install.html#build-your-own-docker-image>`__
-      - .. raw:: html
-
-          <a href="https://github.com/ROCm/llama.cpp"><i class="fab fa-github fa-lg"></i></a>
-
-    * - `FlashInfer <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/flashinfer-compatibility.html>`__
-      - .. raw:: html
-
-          <a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/flashinfer-install.html"><i class="fas fa-link fa-lg"></i></a>
-      - 
-        - `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/flashinfer-install.html#use-a-prebuilt-docker-image-with-flashinfer-pre-installed>`__
-        - `ROCm Base Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/flashinfer-install.html#build-your-own-docker-image>`__
-      - .. raw:: html
-
-          <a href="https://github.com/ROCm/flashinfer"><i class="fab fa-github fa-lg"></i></a>
-
 Learn how to use your ROCm deep learning environment for training, fine-tuning, inference, and performance optimization
 through the following guides.

@@ -44,7 +44,7 @@ Setting up the base implementation environment

   .. code-block:: shell

-      rocm-smi --showproductname
+      amd-smi static --board

 #. Check that your GPUs are available to PyTorch.

@@ -65,8 +65,8 @@ Setting up the base implementation environment

 .. tip::

-   During training and inference, you can check the memory usage by running the ``rocm-smi`` command in your terminal.
-   This tool helps you see shows which GPUs are involved.
+   During training and inference, you can check the memory usage by running the ``amd-smi`` command in your terminal.
+   This tool helps you see which GPUs are involved.


 .. _fine-tuning-llms-multi-gpu-hugging-face-accelerate:
@@ -91,10 +91,10 @@ Now, it's important to adjust how you load the model. Add the ``device_map`` par

   ...
   base_model_name = "meta-llama/Llama-2-7b-chat-hf"
-   
+
   # Load base model to GPU memory
   base_model = AutoModelForCausalLM.from_pretrained(
-           base_model_name, 
+           base_model_name,
           device_map = "auto",
           trust_remote_code = True)
   ...
@@ -130,7 +130,7 @@ After loading the model in this way, the model is fully ready to use the resourc
 torchtune for fine-tuning and inference
 =============================================

-`torchtune <https://pytorch.org/torchtune/main/>`_ is a PyTorch-native library for easy single and multi-GPU 
+`torchtune <https://pytorch.org/torchtune/main/>`_ is a PyTorch-native library for easy single and multi-GPU
 model fine-tuning and inference with LLMs.

 #. Install torchtune using pip.
@@ -139,7 +139,7 @@ model fine-tuning and inference with LLMs.

      # Install torchtune with PyTorch release 2.2.2+
      pip install torchtune
-      
+
      # To confirm that the package is installed correctly
      tune --help

@@ -148,12 +148,12 @@ model fine-tuning and inference with LLMs.
   .. code-block:: shell

      usage: tune [-h] {download,ls,cp,run,validate} ...
-      
+
      Welcome to the TorchTune CLI!
-      
+
      options:
        -h, --help            show this help message and exit
-      
+
      subcommands:
        {download,ls,cp,run,validate}

@@ -194,11 +194,11 @@ model fine-tuning and inference with LLMs.
        apply_lora_to_output: False
        lora_rank: 8
        lora_alpha: 16
-      
+
      tokenizer:
        _component_: torchtune.models.llama2.llama2_tokenizer
        path: /tmp/Llama-2-7b-hf/tokenizer.model
-      
+
      # Dataset and sampler
      dataset:
        _component_: torchtune.datasets.alpaca_cleaned_dataset
@@ -44,20 +44,19 @@ Setting up the base implementation environment

   .. code-block:: shell

-      rocm-smi --showproductname
+      amd-smi static --board

   Your output should look like this:

   .. code-block:: shell

-      ============================ ROCm System Management Interface ============================
-      ====================================== Product Info ======================================
-      GPU[0]          : Card Series:          AMD Instinct MI300X OAM
-      GPU[0]          : Card model:           0x74a1
-      GPU[0]          : Card vendor:          Advanced Micro Devices, Inc. [AMD/ATI]
-      GPU[0]          : Card SKU:             MI3SRIOV
-      ==========================================================================================
-      ================================== End of ROCm SMI Log ===================================
+      GPU: 0
+         BOARD:
+            MODEL_NUMBER: 102-G39203-0B
+            PRODUCT_SERIAL: PCB079220-1150
+            FRU_ID: 113-AMDG392030B04-100-300000097H
+            PRODUCT_NAME: AMD Instinct MI325 OAM
+            MANUFACTURER_NAME: AMD

 #. Check that your GPUs are available to PyTorch.

@@ -94,13 +93,13 @@ Setting up the base implementation environment
      pip install -r requirements-dev.txt
      cmake -DBNB_ROCM_ARCH="gfx942" -DCOMPUTE_BACKEND=hip -S .
      python setup.py install
-      
+
      # To leverage the SFTTrainer in TRL for model fine-tuning.
      pip install trl
-      
+
      # To leverage PEFT for efficiently adapting pre-trained language models .
      pip install peft
-      
+
      # Install the other dependencies.
      pip install transformers datasets huggingface-hub scipy

@@ -132,7 +131,7 @@ Download the base model and fine-tuning dataset

   .. note::

-      You can also use the `NousResearch Llama-2-7b-chat-hf <https://huggingface.co/NousResearch/Llama-2-7b-chat-hf>`_ 
+      You can also use the `NousResearch Llama-2-7b-chat-hf <https://huggingface.co/NousResearch/Llama-2-7b-chat-hf>`_
      as a substitute. It has the same model weights as the original.

 #. Run the following code to load the base model and tokenizer.
@@ -141,14 +140,14 @@ Download the base model and fine-tuning dataset

      # Base model and tokenizer names.
      base_model_name = "meta-llama/Llama-2-7b-chat-hf"
-      
+
      # Load base model to GPU memory.
      device = "cuda:0"
      base_model = AutoModelForCausalLM.from_pretrained(base_model_name, trust_remote_code = True).to(device)
-      
+
      # Load tokenizer.
      tokenizer = AutoTokenizer.from_pretrained(
-              base_model_name, 
+              base_model_name,
              trust_remote_code = True)
      tokenizer.pad_token = tokenizer.eos_token
      tokenizer.padding_side = "right"
@@ -162,10 +161,10 @@ Download the base model and fine-tuning dataset
      # Dataset for fine-tuning.
      training_dataset_name = "mlabonne/guanaco-llama2-1k"
      training_dataset = load_dataset(training_dataset_name, split = "train")
-      
+
      # Check the data.
      print(training_dataset)
-      
+
      # Dataset 11 is a QA sample in English.
      print(training_dataset[11])

@@ -252,8 +251,8 @@ Compare the number of trainable parameters and training time under the two diffe
                    dataset_text_field = "text",
                    tokenizer = tokenizer,
                    args = training_arguments
-            ) 
-            
+            )
+
            # Run the trainer.
            sft_trainer.train()

@@ -286,7 +285,7 @@ Compare the number of trainable parameters and training time under the two diffe
                    if param.requires_grad:
                        trainable_params += param.numel()
                print(f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param:.2f}")
-            
+
            sft_trainer.peft_config = None
            print_trainable_parameters(sft_trainer.model)

@@ -309,8 +308,8 @@ Compare the number of trainable parameters and training time under the two diffe
                    dataset_text_field = "text",
                    tokenizer = tokenizer,
                    args = training_arguments
-            ) 
-            
+            )
+
            # Training.
            trainer_full.train()

@@ -349,7 +348,7 @@ store, and load.

         # PEFT adapter name.
         adapter_name = "llama-2-7b-enhanced-adapter"
-         
+
         # Save PEFT adapter.
         sft_trainer.model.save_pretrained(adapter_name)

@@ -359,21 +358,21 @@ store, and load.

         # Access adapter directory.
         cd llama-2-7b-enhanced-adapter
-         
+
         # List all adapter files.
         README.md  adapter_config.json  adapter_model.safetensors

   .. tab-item:: Saving a fully fine-tuned model
      :sync: without

-      If you're not using LoRA and PEFT so there is no PEFT LoRA configuration used for training, use the following code 
+      If you're not using LoRA and PEFT so there is no PEFT LoRA configuration used for training, use the following code
      to save your fine-tuned model to your system.

      .. code-block:: python

         # Fully fine-tuned model name.
         new_model_name = "llama-2-7b-enhanced"
-         
+
         # Save the fully fine-tuned model.
         full_trainer.model.save_pretrained(new_model_name)

@@ -383,7 +382,7 @@ store, and load.

         # Access new model directory.
         cd llama-2-7b-enhanced
-         
+
         # List all model files.
         config.json                       model-00002-of-00006.safetensors  model-00005-of-00006.safetensors
         generation_config.json            model-00003-of-00006.safetensors  model-00006-of-00006.safetensors
@@ -412,26 +411,26 @@ Let's look at achieving model inference using these types of models.

   .. tab-item:: Inference using PEFT adapters

-      To use PEFT adapters like a normal transformer model, you can run the generation by loading a base model along with PEFT 
+      To use PEFT adapters like a normal transformer model, you can run the generation by loading a base model along with PEFT
      adapters as follows.

      .. code-block:: python

         from peft import PeftModel
         from transformers import AutoModelForCausalLM
-         
+
         # Set the path of the model or the name on Hugging face hub
         base_model_name = "meta-llama/Llama-2-7b-chat-hf"
-         
+
         # Set the path of the adapter
         adapter_name = "Llama-2-7b-enhanced-adpater"
-         
-         # Load base model 
+
+         # Load base model
         base_model = AutoModelForCausalLM.from_pretrained(base_model_name)
-         
-         # Adapt the base model with the adapter 
+
+         # Adapt the base model with the adapter
         new_model = PeftModel.from_pretrained(base_model, adapter_name)
-         
+
         # Then, run generation as the same with a normal model outlined in 2.1

      The PEFT library provides a ``merge_and_unload`` method, which merges the adapter layers into the base model. This is
@@ -439,13 +438,13 @@ Let's look at achieving model inference using these types of models.

      .. code-block:: python

-         # Load base model 
+         # Load base model
         base_model = AutoModelForCausalLM.from_pretrained(base_model_name)
-         
-         # Adapt the base model with the adapter 
+
+         # Adapt the base model with the adapter
         new_model = PeftModel.from_pretrained(base_model, adapter_name)
-         
-         # Merge adapter 
+
+         # Merge adapter
         model = model.merge_and_unload()

         # Save the merged model into local
@@ -461,25 +460,25 @@ Let's look at achieving model inference using these types of models.

         # Import relevant class for loading model and tokenizer
         from transformers import AutoTokenizer, AutoModelForCausalLM
-         
+
         # Set the pre-trained model name on Hugging face hub
         model_name = "meta-llama/Llama-2-7b-chat-hf"
-         
-         # Set device type 
+
+         # Set device type
         device = "cuda:0"
-         
-         # Load model and tokenizer 
+
+         # Load model and tokenizer
         model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
         tokenizer = AutoTokenizer.from_pretrained(model_name)
-         
-         # Input prompt encoding 
+
+         # Input prompt encoding
         query = "What is a large language model?"
         inputs = tokenizer.encode(query, return_tensors="pt").to(device)
-         
-         # Token generation  
-         outputs = model.generate(inputs) 
-         
-         # Outputs decoding 
+
+         # Token generation
+         outputs = model.generate(inputs)
+
+         # Outputs decoding
         print(tokenizer.decode(outputs[0]))

      In addition, pipelines from Transformers offer simple APIs to use pre-trained models for different tasks, including
@@ -490,14 +489,14 @@ Let's look at achieving model inference using these types of models.

         # Import relevant class for loading model and tokenizer
         from transformers import pipeline
-         
+
         # Set the path of your model or the name on Hugging face hub
         model_name_or_path = "meta-llama/Llama-2-7b-chat-hf"
-         
-         # Set pipeline 
+
+         # Set pipeline
         # A positive device value will run the model on associated CUDA device id
         pipe = pipeline("text-generation", model=model_name_or_path, device=0)
-         
+
         # Token generation
         print(pipe("What is a large language model?")[0]["generated_text"])

@@ -25,6 +25,5 @@ In this guide, you'll learn how to use ROCm for AI:

 - :doc:`Inference optimization <inference-optimization/index>`

-
 To learn about ROCm for HPC applications and scientific computing, see
 :doc:`../rocm-for-hpc/index`.
@@ -24,94 +24,102 @@ performance.
   :alt: Attention module of a large language module utilizing tiling
   :align: center

+Installation prerequisites
+----------------------------
+
+Before installing Flash Attention 2, ensure the following are available:
+
+*  ROCm-enabled PyTorch
+*  Triton
+
+These can be installed by following the official
+`PyTorch installation guide <https://pytorch.org/get-started/locally/>`_. Alternatively, for a simpler setup, you can use a preconfigured
+:ref:`ROCm PyTorch Docker image <using-docker-with-pytorch-pre-installed>`, which already includes the required libraries.
+
 Installing Flash Attention 2 
 ----------------------------

-ROCm provides two different implementations of Flash Attention 2 modules. They can be deployed interchangeably:
+`Flash Attention <https://github.com/Dao-AILab/flash-attention>`_ supports two backend implementations on AMD GPUs.

-*  ROCm `Composable Kernel <https://github.com/ROCm/composable_kernel/tree/develop/example/01_gemm>`_
-   (CK) Flash Attention 2
+*  `Composable Kernel (CK) <https://github.com/ROCm/composable_kernel>`__ - the default backend
+*  `OpenAI Triton <https://github.com/triton-lang/triton>`__ - an alternative backend

-*  `OpenAI Triton <https://triton-lang.org/main/index.html>`_ Flash Attention 2
+You can switch between these backends using the environment variable ``FLASH_ATTENTION_TRITON_AMD_ENABLE``:

-.. tab-set::
+``FLASH_ATTENTION_TRITON_AMD_ENABLE="FALSE"``
+→ Use Composable Kernel (CK) backend (Flash Attention 2)

-   .. tab-item:: CK Flash Attention 2
+``FLASH_ATTENTION_TRITON_AMD_ENABLE="TRUE"``
+→ Use OpenAI Triton backend (Flash Attention 2)

-      To install CK Flash Attention 2, use the following commands.
+To install Flash Attention 2, use the following commands:

-      .. code-block:: shell
+.. code-block:: shell

-         # Install from source
-         git clone https://github.com/ROCm/flash-attention.git
-         cd flash-attention/
-         GPU_ARCHS=gfx942 python setup.py install #MI300 Series
+   git clone https://github.com/Dao-AILab/flash-attention.git
+   cd flash-attention/
+   pip install ninja

-      Hugging Face Transformers can easily deploy the CK Flash Attention 2 module by passing an argument
-      ``attn_implementation="flash_attention_2"`` in the ``from_pretrained`` class.
+   # To install the CK backend flash attention
+   python setup.py install 

-      .. code-block:: python
+   # To install the Triton backend flash attention
+   FLASH_ATTENTION_TRITON_AMD_ENABLE="TRUE" python setup.py install 

-         import torch
-         from transformers import AutoModelForCausalLM, AutoTokenizer
-         device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-         model_name = "NousResearch/Meta-Llama-3-8B"
+   # To install both CK and Triton backend flash attention
+   FLASH_ATTENTION_TRITON_AMD_ENABLE=TRUE && FLASH_ATTENTION_SKIP_CK_BUILD=FALSE python setup.py install

-         tokenizer = AutoTokenizer.from_pretrained(model_name, torch_dtype=torch.float16, use_fast=False)
-         inputs = tokenizer('Today is', return_tensors='pt').to(device)
+For detailed installation instructions, see `Flash Attention <https://github.com/Dao-AILab/flash-attention>`_.

-         model_eager = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, attn_implementation="eager").cuda(device)
-         model_ckFAv2 = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, attn_implementation="flash_attention_2").cuda(device)
+Benchmarking Flash Attention 2 
+------------------------------

-         print("eager GQA: ", tokenizer.decode(model_eager.generate(**inputs, max_new_tokens=10)[0], skip_special_tokens=True))
-         print("ckFAv2 GQA: ", tokenizer.decode(model_ckFAv2.generate(**inputs, max_new_tokens=10)[0], skip_special_tokens=True))
+Benchmark scripts to evaluate the performance of Flash Attention 2 are stored in the ``flash-attention/benchmarks/`` directory.

-         #  eager GQA:  Today is the day of the Lord, and we are the
-         # ckFAv2 GQA: Today is the day of the Lord, and we are the
+To benchmark the CK backend 

-   .. tab-item:: Triton Flash Attention 2
+.. code-block:: shell

-      The Triton Flash Attention 2 module is implemented in Python and uses OpenAI’s JIT compiler. This module has been
-      upstreamed into the vLLM serving toolkit, discussed in :doc:'llm-inference-frameworks'. 
+   cd flash-attention/benchmarks
+   pip install transformers einops ninja

-      1. To install Triton Flash Attention 2 and run the benchmark, use the following commands.
+   python3 benchmark_flash_attention.py 

-         .. code-block:: shell
+To benchmark the Triton backend

-            # Install from the source
-            pip uninstall pytorch-triton-rocm triton -y
-            git clone https://github.com/ROCm/triton.git 
-            cd triton/python
-            GPU_ARCHS=gfx942 python setup.py install #MI300 series
-            pip install matplotlib pandas
+.. code-block:: shell

-      2. To test, run the Triton Flash Attention 2 performance benchmark.
+   FLASH_ATTENTION_TRITON_AMD_ENABLE="TRUE" python3 benchmark_flash_attention.py

-         .. code-block:: shell
-         
-            # Test the triton FA v2 kernel
-            python https://github.com/ROCm/triton/blob/triton-mlir/python/perf-kernels/flash-attention.py
-            # Results (Okay to release TFLOPS number ???)
-            fused-attention-fwd-d128:
-                BATCH    HQ    HK  N_CTX_Q  N_CTX_K      TFLOPS
-            0    16.0  16.0  16.0   1024.0   1024.0  287.528411
-            1     8.0  16.0  16.0   2048.0   2048.0  287.490806
-            2     4.0  16.0  16.0   4096.0   4096.0  345.966031
-            3     2.0  16.0  16.0   8192.0   8192.0  361.369510
-            4     1.0  16.0  16.0  16384.0  16384.0  356.873720
-            5     2.0  48.0  48.0   1024.0   1024.0  216.916235
-            6     2.0  48.0  48.0   2048.0   1024.0  271.027578
-            7     2.0  48.0  48.0   4096.0   8192.0  337.367372
-            8     2.0  48.0  48.0   8192.0   4096.0  363.481649
-            9     2.0  48.0  48.0  16384.0   8192.0  375.013622
-            10    8.0  16.0  16.0   1989.0  15344.0  321.791333
-            11    4.0  16.0  16.0   4097.0    163.0  122.104888
-            12    2.0  16.0  16.0   8122.0   2159.0  337.060283
-            13    1.0  16.0  16.0  16281.0      7.0    5.234012
-            14    2.0  48.0  48.0   1021.0   1020.0  214.657425
-            15    2.0  48.0  48.0   2001.0   2048.0  314.429118
-            16    2.0  48.0  48.0   3996.0   9639.0  330.411368
-            17    2.0  48.0  48.0   8181.0   1021.0  324.614980
+Using Flash Attention 2 
+-----------------------
+
+.. code-block:: python
+
+   import torch
+   from transformers import AutoModelForCausalLM, AutoTokenizer
+   device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+   model_name = "NousResearch/Llama-3.2-1B"
+
+   tokenizer = AutoTokenizer.from_pretrained(model_name, dtype=torch.bfloat16, use_fast=False)
+   inputs = tokenizer('Today is', return_tensors='pt').to(device)
+
+   model_eager = AutoModelForCausalLM.from_pretrained(model_name, dtype=torch.bfloat16, attn_implementation="eager").cuda(device)
+   model_ckFAv2 = AutoModelForCausalLM.from_pretrained(model_name, dtype=torch.bfloat16, attn_implementation="flash_attention_2").cuda(device)
+   model_eager.generation_config.pad_token_id = model_eager.generation_config.eos_token_id
+   model_ckFAv2.generation_config.pad_token_id = model_ckFAv2.generation_config.eos_token_id
+
+   print("eager\n GQA: ", tokenizer.decode(model_eager.generate(**inputs, max_new_tokens=22)[0], skip_special_tokens=True, do_sample=False, num_beams=1))
+   print("ckFAv2\n GQA: ", tokenizer.decode(model_ckFAv2.generate(**inputs, max_new_tokens=22)[0], skip_special_tokens=True, do_sample=False, num_beams=1))
+
+The outputs from eager mode and FlashAttention-2 are identical, although their performance behavior differs.
+
+.. code-block:: shell
+
+   eager
+   GQA:  Today is the 10th anniversary of the 9/11 attacks. I remember that day like it was yesterday.
+   ckFAv2
+   GQA:  Today is the 10th anniversary of the 9/11 attacks. I remember that day like it was yesterday.

 xFormers
 ========
@@ -15,10 +15,9 @@ using PyTorch. It delves into specific workloads such as
 :ref:`model inference <mi300x-vllm-optimization>`, offering strategies to
 enhance efficiency.

-The following topics highlight :ref:`auto-tunable configurations <mi300x-auto-tune>`
-that streamline optimization as well as advanced techniques like
-:ref:`Triton kernel optimization <mi300x-triton-kernel-performance-optimization>` for
-meticulous tuning.
+The following topics highlight :ref:`auto-tunable configurations <mi300x-auto-tune>` as
+well as :ref:`Triton kernel optimization <mi300x-triton-kernel-performance-optimization>`
+for meticulous tuning.

 Workload tuning strategy
 ========================
@@ -86,27 +85,28 @@ Optimize model inference with vLLM
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

 vLLM provides tools and techniques specifically designed for efficient model
-inference on AMD Instinct MI300X GPUs. See :ref:`fine-tuning-llms-vllm`
-for installation guidance. Optimizing performance with vLLM
-involves configuring tensor parallelism, leveraging advanced features, and
-ensuring efficient execution. Here’s how to optimize vLLM performance:
+inference on AMD Instinct GPUs. See the official `vLLM installation docs
+<https://docs.vllm.ai/en/latest/getting_started/installation/gpu.html>`__ for
+installation guidance. Optimizing performance with vLLM involves configuring
+tensor parallelism, leveraging advanced features, and ensuring efficient
+execution.

-* Tensor parallelism: Configure the
-  :ref:`tensor-parallel-size parameter <mi300x-vllm-multiple-gpus>` to distribute
-  tensor computations across multiple GPUs. Adjust parameters such as
-  ``batch-size``, ``input-len``, and ``output-len`` based on your workload.
-
-* Configuration for vLLM: Set :ref:`parameters <mi300x-vllm-optimization>`
-  according to workload requirements. Benchmark performance to understand
-  characteristics and identify bottlenecks.
+* Configuration for vLLM: Set engine arguments according to workload
+  requirements.

 * Benchmarking and performance metrics: Measure latency and throughput to
  evaluate performance.

+.. seealso::
+
+   See :doc:`vllm-optimization` to learn more about vLLM performance
+   optimization techniques.
+
 .. _mi300x-auto-tune:

 Auto-tunable configurations
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
 Auto-tunable configurations can significantly streamline performance
 optimization by automatically adjusting parameters based on workload
 characteristics. For example:
@@ -120,8 +120,7 @@ characteristics. For example:
  your specific hardware.

 * Triton: Use :ref:`Triton’s auto-tuning features <mi300x-autotunable-kernel-config>`
-  to explore various kernel configurations and automatically select the
-  best-performing ones.
+  to explore various kernel configurations and select the best-performing ones.

 Manual tuning
 ^^^^^^^^^^^^^
@@ -328,380 +327,21 @@ hardware counters are also included.

   ROCm Systems Profiler timeline trace example.

-.. _mi300x-vllm-optimization:
-
 vLLM performance optimization
 =============================

-vLLM is a high-throughput and memory efficient inference and serving engine for large language models that has gained traction in the AI community for
-its performance and ease of use. See :ref:`fine-tuning-llms-vllm` for a primer on vLLM with ROCm.
-
-Performance environment variables
---------------------------------
-
-The following performance tips are not *specific* to vLLM -- they are general
-but relevant in this context. You can tune the following vLLM parameters to
-achieve optimal request latency and throughput performance.
-
-* As described in `Environment variables (MI300X)
-  <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html#environment-variables>`_,
-  the environment variable ``HIP_FORCE_DEV_KERNARG`` can improve vLLM
-  performance. Set it to ``export HIP_FORCE_DEV_KERNARG=1``.
-
-* Set the :ref:`RCCL environment variable <mi300x-rccl>` ``NCCL_MIN_NCHANNELS``
-  to ``112`` to increase the number of channels on MI300X to potentially improve
-  performance.
-
-* Set the environment variable ``TORCH_BLAS_PREFER_HIPBLASLT=1`` to use hipBLASLt to improve performance.
-
-Auto-tuning using PyTorch TunableOp
------------------------------------
-
-Since vLLM is based on the PyTorch framework, PyTorch TunableOp can be used for auto-tuning. 
-You can run auto-tuning with TunableOp in two simple steps without modifying your code:
-
-* Enable TunableOp and tuning. Optionally, enable verbose mode:
-
-  .. code-block:: shell
-
-     PYTORCH_TUNABLEOP_ENABLED=1 PYTORCH_TUNABLEOP_VERBOSE=1 your_vllm_script.sh
-
-* Enable TunableOp and disable tuning and measure.
-
-  .. code-block:: shell
-
-     PYTORCH_TUNABLEOP_ENABLED=1 PYTORCH_TUNABLEOP_TUNING=0 your_vllm_script.sh
-
-Learn more about TunableOp in the :ref:`PyTorch TunableOp <mi300x-tunableop>` section.
-
-Performance tuning based on vLLM engine configurations
-------------------------------------------------------
-
-The following subsections describe vLLM-specific configurations for performance tuning.
-You can tune the following vLLM parameters to achieve optimal performance.
-
-*  ``tensor_parallel_size``
-
-*  ``gpu_memory_utilization``
-
-*  ``dtype``
-
-*  ``enforce_eager``
-
-*  ``kv_cache_dtype``
-
-*  ``input_len``
-
-*  ``output_len``
-
-*  ``max_num_seqs``
-
-*  ``num_scheduler_steps``
-
-*  ``max_model_len``
-
-*  ``enable_chunked_prefill``
-
-*  ``distributed_executor_backend``
-
-*  ``max_seq_len_to_capture``
-
-Refer to `vLLM documentation <https://docs.vllm.ai/en/latest/models/performance.html>`_
-for additional performance tips. :ref:`fine-tuning-llms-vllm` describes vLLM
-usage with ROCm.
-
-ROCm provides a prebuilt optimized Docker image for validating the performance
-of LLM inference with vLLM on MI300X Series GPUs. The Docker image includes
-ROCm, vLLM, and PyTorch. For more information, see
-:doc:`/how-to/rocm-for-ai/inference/benchmark-docker/vllm`.
-
-.. _mi300x-vllm-throughput-measurement:
-
-Evaluating performance by throughput measurement
-------------------------------------------------
-
-This tuning guide evaluates the performance of LLM inference workloads by measuring throughput in tokens per second (TPS). Throughput can be assessed using both real-world and synthetic data, depending on your evaluation goals.
-
-Refer to the benchmarking script located at ``benchmarks/benchmark_throughput.py`` in the `vLLM repository <https://github.com/ROCm/vllm/blob/main/benchmarks/benchmark_throughput.py>`_.
-Use this script to measure throughput effectively. You can assess throughput using real-world and synthetic data, depending on your evaluation goals.
-
-* For realistic performance evaluation, you can use datasets like Hugging Face's
-  ``ShareGPT_V3_unfiltered_cleaned_split.json``. This dataset includes real-world conversational
-  data, making it a good representation of typical use cases for language models. Download it using
-  the following command:
-
-  .. code-block:: shell
-
-     wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
-
-* For standardized benchmarking, you can set fixed input and output token
-  lengths. Synthetic prompts provide consistent benchmarking runs, making it
-  easier to compare performance across different models or configurations.
-  Additionally, a controlled environment simplifies analysis.
-
-By balancing real-world data and synthetic data approaches, you can get a well-rounded understanding of model performance in varied scenarios.
-
-.. _mi300x-vllm-single-node:
-
-Maximizing vLLM instances on a single node
------------------------------------------
-
-The general guideline is to maximize per-node throughput by running as many vLLM instances as possible.
-However, running too many instances might lead to insufficient memory for the KV-cache, which can affect performance.
-
-The Instinct MI300X GPU is equipped with 192 GB of HBM3 memory capacity and bandwidth.
-For models that fit in one GPU -- to maximize the accumulated throughput -- you can run as many as eight vLLM instances
-simultaneously on one MI300X node (with eight GPUs). To do so, use the GPU isolation environment
-variable ``CUDA_VISIBLE_DEVICES``.
-
-For example, this script runs eight instances of vLLM for throughput benchmarking at the same time
-with a model that can fit in one GPU:
-
-.. code-block:: shell
-
-   for i in $(seq 0 7);
-   do
-       CUDA_VISIBLE_DEVICES="$i" python3 /app/vllm/benchmarks/benchmark_throughput.py -tp 1 --dataset "/path/to/dataset/ShareGPT_V3_unfiltered_cleaned_split.json" --model /path/to/model &
-   done
-
-The total throughput achieved by running ``N`` instances of vLLM is generally much higher than running a
-single vLLM instance across ``N`` GPUs simultaneously (that is, configuring ``tensor_parallel_size`` as N or
-using the ``-tp`` N option, where ``1 < N ≤ 8``).
-
-vLLM on MI300X GPUs can run a variety of model weights, including Llama 2 (7b, 13b, 70b), Llama 3 (8b, 70b), Qwen2 (7b, 72b), Mixtral-8x7b, Mixtral-8x22b, and so on.
-Notable configurations include Llama2-70b and Llama3-70b models on a single MI300X GPU, and the Llama3.1 405b model can fit on one single node with 8 MI300X GPUs.
-
-.. _mi300x-vllm-gpu-memory-utilization:
-
-Configure the gpu_memory_utilization parameter
----------------------------------------------
-
-There are two ways to increase throughput by configuring ``gpu-memory-utilization`` parameter.
-
-1. Increase ``gpu-memory-utilization`` to improve the throughput for a single instance as long as
-   it does not incur HIP or CUDA Out Of Memory. The default ``gpu-memory-utilization`` is 0.9.
-   You can set it to ``>0.9`` and ``<1``.
-
-   For example, below benchmarking command set the ``gpu-memory-utilization`` as 0.98, or 98%.
-
-   .. code-block:: shell
-
-      /vllm-workspace/benchmarks/benchmark_throughput.py --gpu-memory-utilization 0.98 --input-len 1024 --output-len 128 --model /path/to/model
-
-2. Decrease ``gpu-memory-utilization`` to maximize the number of vLLM instances on the same GPU.
-
-   Specify GPU memory utilization to run as many instances of vLLM as possible on a single
-   GPU. However, too many instances can result in no memory for KV-cache. For small models, run
-   multiple instances of vLLM on the same GPU by specifying a smaller ``gpu-memory-utilization`` -- as
-   long as it would not cause HIP Out Of Memory. 
-
-   For example, run two instances of the Llama3-8b model at the same time on a single GPU by specifying
-   ``--gpu-memory-utilization`` to 0.4 (40%) as follows (on GPU ``0``):
-
-   .. code-block:: shell
-
-      CUDA_VISIBLE_DEVICES=0 python3 /vllm-workspace/benchmarks/benchmark_throughput.py --gpu-memory-utilization 0.4 
-      --dataset "/path/to/dataset/ShareGPT_V3_unfiltered_cleaned_split.json" --model /path/to/model &
-
-      CUDA_VISIBLE_DEVICES=0 python3 /vllm-workspace/benchmarks/benchmark_throughput.py --gpu-memory-utilization 0.4 
-      --dataset "/path/to/dataset/ShareGPT_V3_unfiltered_cleaned_split.json" --model /path/to/model &
-
-See :ref:`vllm-engine-args` for other performance suggestions.
-
-.. _mi300x-vllm-multiple-gpus:
-
-Run vLLM on multiple GPUs
-------------------------
-
-The two main reasons to use multiple GPUs are:
-
-*  The model size is too big to run vLLM using one GPU as it results HIP Out of Memory.
-
-*  To achieve better latency when using a single GPU is not desirable.
-
-To run one vLLM instance on multiple GPUs, use the ``-tp`` or ``--tensor-parallel-size`` option to
-specify multiple GPUs. Optionally, use the ``CUDA_VISIBLE_DEVICES`` environment variable to specify
-the GPUs.
-
-For example, you can use two GPUs to start an API server on port 8000:
-
-.. code-block:: shell
-
-   python -m vllm.entrypoints.api_server --model /path/to/model --dtype
-   float16 -tp 2 --port 8000 &
-
-To achieve both latency and throughput performance for serving, you can run multiple API servers on
-different GPUs by specifying different ports for each server and use ``CUDA_VISIBLE_DEVICES`` to
-specify the GPUs for each server, for example:
-
-.. code-block:: shell
-
-   CUDA_VISIBLE_DEVICES=0,1 python -m vllm.entrypoints.api_server --model
-   /path/to/model --dtype float16 -tp 2 --port 8000 &
-
-   CUDA_VISIBLE_DEVICES=2,3 python -m vllm.entrypoints.api_server --model
-   /path/to/model --dtype float16 -tp 2 --port 8001 &
-
-Choose an attention backend
---------------------------
-
-vLLM on ROCm supports two attention backends, each suitable for different use cases and performance
-requirements:
-
- **Triton Flash Attention** - For benchmarking, run vLLM scripts at
-  least once as a warm-up step so Triton can perform auto-tuning before
-  collecting benchmarking numbers. This is the default setting.
-
- **Composable Kernel (CK) Flash Attention** - To use CK Flash Attention, specify
-  the environment variable as ``export VLLM_USE_TRITON_FLASH_ATTN=0``.
-
-
-Refer to :ref:`Model acceleration libraries <acceleration-flash-attention>`
-to learn more about Flash Attention with Triton or CK backends.
-
-.. _vllm-engine-args:
-
-vLLM engine arguments
---------------------
-
-The following are configuration suggestions to potentially improve performance with vLLM. See
-`vLLM's engine arguments documentation <https://docs.vllm.ai/en/latest/serving/engine_args.html>`_
-for a full list of configurable engine arguments.
-
-Configure the max-num-seqs parameter
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Increase the ``max-num-seqs`` parameter from the default ``256`` to ``512`` (``--max-num-seqs
-512``). This increases the maximum number of sequences per iteration and can improve throughput.
-
-Use the float16 dtype
-^^^^^^^^^^^^^^^^^^^^^
-
-The default data type (``dtype``) is specified in the model’s configuration file. For instance, some models use ``torch.bfloat16`` as their default ``dtype``.
-Use float16 (``--dtype float16``) for better performance.
-
-Multi-step scheduling
-^^^^^^^^^^^^^^^^^^^^^
-
-Setting ``num-scheduler-steps`` for multi-step scheduling can increase performance. Set it between 10 to 15 (``--num-scheduler-steps 10``).
-
-Distributed executor backend
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-The vLLM supports two modes of distributed executor backend: ``ray`` and ``mp``. When using the `<https://github.com/ROCm/vllm>`__ fork, using the ``mp``
-backend (``--distributed_executor_backend mp``) is recommended.
-
-Graph mode max-seq-len-to-capture
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Maximum sequence length covered by CUDA graphs. In the default mode (where ``enforce_eager`` is ``False``), when a sequence has context length
-larger than this, vLLM engine falls back to eager mode. The default is 8192.
-
-When working with models that support long context lengths, set the parameter ``--max-seq-len-to-capture`` to 16384.
-See this `vLLM blog <https://blog.vllm.ai/2024/10/23/vllm-serving-amd.html>`__ for details.
-
-An example of long context length model is Qwen2-7b.
-
-Whether to enable chunked prefill
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Another vLLM performance tip is to enable chunked prefill to improve
-throughput. Chunked prefill allows large prefills to be chunked into
-smaller chunks and batched together with decode requests.
-
-You can enable the feature by specifying ``--enable-chunked-prefill`` in the
-command line or setting ``enable_chunked_prefill=True`` in the LLM
-constructor. 
-
-As stated in `vLLM's documentation, <https://docs.vllm.ai/en/latest/models/performance.html#chunked-prefill>`__,
-you can tune the performance by changing ``max_num_batched_tokens``. By
-default, it is set to 512 and optimized for ITL (inter-token latency).
-Smaller ``max_num_batched_tokens`` achieves better ITL because there are
-fewer prefills interrupting decodes.
-Higher ``max_num_batched_tokens`` achieves better TTFT (time to the first
-token) as you can put more prefill to the batch.
-
-You might experience noticeable throughput improvements when
-benchmarking on a single GPU or 8 GPUs using the vLLM throughput
-benchmarking script along with the ShareGPT dataset as input.
-
-In the case of fixed ``input-len``/``output-len``, for some configurations,
-enabling chunked prefill increases the throughput. For some other
-configurations, the throughput may be worse and elicit a need to tune
-parameter ``max_num_batched_tokens`` (for example, increasing ``max_num_batched_tokens`` value to 4096 or larger).
-
-.. note::
-
-   Chunked prefill is no longer recommended. See the vLLM blog: `Serving LLMs on AMD MI300X: Best Practices <https://blog.vllm.ai/2024/10/23/vllm-serving-amd.html>`_ (October 2024).
-
-Quantization support
---------------------
-
-Quantization reduces the precision of the model’s weights and activations, which significantly decreases the memory footprint.
-``fp8(w8a8)`` and ``AWQ`` quantization are supported for ROCm.
-
-FP8 quantization
-^^^^^^^^^^^^^^^^^
-
-`<https://github.com/ROCm/vllm>`__ supports FP8 (8-bit floating point) weight and activation quantization using hardware acceleration on the Instinct MI300X.
-Quantization of models with FP8 allows for a 2x reduction in model memory requirements and up to a 1.6x improvement in throughput with minimal impact on accuracy.
-
-AMD publishes Quark Quantized OCP FP8 models on Hugging Face. For example:
-
-* `Llama-3.1-8B-Instruct-FP8-KV <https://huggingface.co/amd/Llama-3.1-8B-Instruct-FP8-KV>`__
-* `Llama-3.1-70B-Instruct-FP8-KV <https://huggingface.co/amd/Llama-3.1-70B-Instruct-FP8-KV>`__
-* `Llama-3.1-405B-Instruct-FP8-KV <https://huggingface.co/amd/Llama-3.1-405B-Instruct-FP8-KV>`__
-* `Mixtral-8x7B-Instruct-v0.1-FP8-KV <https://huggingface.co/amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV>`__
-* `Mixtral-8x22B-Instruct-v0.1-FP8-KV <https://huggingface.co/amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV>`__
-
-To enable vLLM benchmarking to run on fp8 quantized models, use the ``--quantization`` parameter with value ``fp8`` (``--quantization fp8``).
-
-AWQ quantization
-^^^^^^^^^^^^^^^^
-
-You can quantize your own models by installing AutoAWQ or picking one of the 400+ models on Hugging Face. Be aware that
-that AWQ support in vLLM is currently underoptimized.
-
-To enable vLLM to run on ``awq`` quantized models, using ``--quantization`` parameter with ``awq`` (``--quantization awq``).
-
-You can find more specifics in the `vLLM AutoAWQ documentation <https://docs.vllm.ai/en/stable/quantization/auto_awq.html>`_.
-
-fp8 kv-cached-dtype
-^^^^^^^^^^^^^^^^^^^^^^^
-
-Using ``fp8 kv-cache dtype`` can improve performance as it reduces the size
-of ``kv-cache``. As a result, it reduces the cost required for reading and
-writing the ``kv-cache``.
-
-To use this feature, specify ``--kv-cache-dtype`` as ``fp8``.
-
-To specify the quantization scaling config, use the
-``--quantization-param-path`` parameter. If the parameter is not specified,
-the default scaling factor of ``1`` is used, which can lead to less accurate
-results. To generate ``kv-cache`` scaling JSON file, see `FP8 KV
-Cache <https://github.com/vllm-project/llm-compressor/blob/main/examples/quantization_kv_cache/README.md>`__
-in the vLLM GitHub repository.
-
-Two sample Llama scaling configuration files are in vLLM for ``llama2-70b`` and
-``llama2-7b``.
-
-If building the vLLM using
-`Dockerfile.rocm <https://github.com/vllm-project/vllm/blob/main/docker/Dockerfile.rocm>`_
-for ``llama2-70b`` scale config, find the file at
-``/vllm-workspace/tests/fp8_kv/llama2-70b-fp8-kv/kv_cache_scales.json`` at
-runtime.
-
-Below is a sample command to run benchmarking with this feature enabled
-for the ``llama2-70b`` model:
-
-.. code-block:: shell
-
-   python3 /vllm-workspace/benchmarks/benchmark_throughput.py --model \
-   /path/to/llama2-70b-model --kv-cache-dtype "fp8" \
-   --quantization-param-path \
-   "/vllm-workspace/tests/fp8_kv/llama2-70b-fp8-kv/kv_cache_scales.json" \
-   --input-len 512 --output-len 256 --num-prompts 500
-
+vLLM is a high-throughput and memory efficient inference and serving engine for
+large language models that has gained traction in the AI community for its
+performance and ease of use. See :doc:`vllm-optimization`, where you'll learn
+how to:
+
+* Enable AITER (AI Tensor Engine for ROCm) to speed up on LLM models.
+* Configure environment variables for optimal HIP, RCCL, and Quick Reduce performance.
+* Select the right attention backend for your workload (AITER MHA/MLA vs. Triton).
+* Choose parallelism strategies (tensor, pipeline, data, expert) for multi-GPU deployments.
+* Apply quantization (``FP8``/``FP4``) to reduce memory usage by 2-4× with minimal accuracy loss.
+* Tune engine arguments (batch size, memory utilization, graph modes) for your use case.
+* Benchmark and scale across single-node and multi-node configurations.

 .. _mi300x-tunableop:

@@ -946,33 +586,33 @@ for details.

  .. code-block:: shell

-     HIP_FORCE_DEV_KERNARG=1  hipblaslt-bench --alpha 1 --beta 0 -r f16_r \
+     HIP_FORCE_DEV_KERNARG=1  hipblaslt-bench --alpha 1 --beta 0 -r f16_r \
     --a_type f16_r --b_type f8_r --compute_type f32_f16_r \
-     --initialization trig_float  --cold_iters 100 --iters 1000 --rotating 256
+     --initialization trig_float  --cold_iters 100 --iters 1000 --rotating 256

 * Example 2: Benchmark forward epilogues and backward epilogues

-  *  ``HIPBLASLT_EPILOGUE_RELU: "--activation_type relu";``
+  *  ``HIPBLASLT_EPILOGUE_RELU: "--activation_type relu";``

-  *  ``HIPBLASLT_EPILOGUE_BIAS: "--bias_vector";``
+  *  ``HIPBLASLT_EPILOGUE_BIAS: "--bias_vector";``

-  *  ``HIPBLASLT_EPILOGUE_RELU_BIAS: "--activation_type relu --bias_vector";``
+  *  ``HIPBLASLT_EPILOGUE_RELU_BIAS: "--activation_type relu --bias_vector";``

-  *  ``HIPBLASLT_EPILOGUE_GELU: "--activation_type gelu";``
+  *  ``HIPBLASLT_EPILOGUE_GELU: "--activation_type gelu";``

  *  ``HIPBLASLT_EPILOGUE_DGELU": --activation_type gelu --gradient";``

-  *  ``HIPBLASLT_EPILOGUE_GELU_BIAS: "--activation_type gelu --bias_vector";``
+  *  ``HIPBLASLT_EPILOGUE_GELU_BIAS: "--activation_type gelu --bias_vector";``

-  *  ``HIPBLASLT_EPILOGUE_GELU_AUX: "--activation_type gelu --use_e";``
+  *  ``HIPBLASLT_EPILOGUE_GELU_AUX: "--activation_type gelu --use_e";``

-  *  ``HIPBLASLT_EPILOGUE_GELU_AUX_BIAS: "--activation_type gelu --bias_vector --use_e";``
+  *  ``HIPBLASLT_EPILOGUE_GELU_AUX_BIAS: "--activation_type gelu --bias_vector --use_e";``

-  *  ``HIPBLASLT_EPILOGUE_DGELU_BGRAD: "--activation_type gelu --bias_vector --gradient";``
+  *  ``HIPBLASLT_EPILOGUE_DGELU_BGRAD: "--activation_type gelu --bias_vector --gradient";``

-  *  ``HIPBLASLT_EPILOGUE_BGRADA: "--bias_vector --gradient --bias_source a";``
+  *  ``HIPBLASLT_EPILOGUE_BGRADA: "--bias_vector --gradient --bias_source a";``

-  *  ``HIPBLASLT_EPILOGUE_BGRADB:  "--bias_vector --gradient --bias_source b";``
+  *  ``HIPBLASLT_EPILOGUE_BGRADB:  "--bias_vector --gradient --bias_source b";``


 hipBLASLt auto-tuning using hipblaslt-bench
@@ -1031,26 +671,26 @@ The tuning tool is a two-step tool. It first runs the benchmark, then it creates

  .. code-block:: python

-     defaultBenchOptions = {"ProblemType": {
-         "TransposeA": 0,
-         "TransposeB": 0,
-         "ComputeInputDataType": "s",
-         "ComputeDataType": "s",
-         "DataTypeC": "s",
-         "DataTypeD": "s",
-         "UseBias": False
-     }, "TestConfig": {
-         "ColdIter": 20,
-         "Iter": 100,
-         "AlgoMethod": "all",
-         "RequestedSolutions": 2, # Only works in AlgoMethod heuristic
-         "SolutionIndex": None, # Only works in AlgoMethod index
-         "ApiMethod": "cpp",
-         "RotatingBuffer": 0,
-     }, "TuningParameters": {
-         "SplitK": [0]
-     }, "ProblemSizes": []}
-     defaultCreateLogicOptions = {}  # Currently unused
+     defaultBenchOptions = {"ProblemType": {
+         "TransposeA": 0,
+         "TransposeB": 0,
+         "ComputeInputDataType": "s",
+         "ComputeDataType": "s",
+         "DataTypeC": "s",
+         "DataTypeD": "s",
+         "UseBias": False
+     }, "TestConfig": {
+         "ColdIter": 20,
+         "Iter": 100,
+         "AlgoMethod": "all",
+         "RequestedSolutions": 2, # Only works in AlgoMethod heuristic
+         "SolutionIndex": None, # Only works in AlgoMethod index
+         "ApiMethod": "cpp",
+         "RotatingBuffer": 0,
+     }, "TuningParameters": {
+         "SplitK": [0]
+     }, "ProblemSizes": []}
+     defaultCreateLogicOptions = {}  # Currently unused

 * ``TestConfig``
   1. ``ColdIter``: This is number the warm-up iterations before starting the kernel benchmark.
@@ -1230,7 +870,7 @@ command:

 .. code-block:: shell

-   merge.py original_dir new_tuned_yaml_dir output_dir 
+   merge.py original_dir new_tuned_yaml_dir output_dir 

 The following table describes the logic YAML files.

@@ -1833,7 +1473,7 @@ de-quantize the ``int4`` key-value from the ``int4`` data type to ``fp16``.

 From the IR snippet, you can see ``i32`` data is loaded from global memory to
 registers (``%190``). With a few element-wise operations in registers, it is
-stored in shared memory (``%269``) for the transpose operation (``%270``), which
+stored in shared memory (``%269``) for the transpose operation (``%270``), which
 needs data movement across different threads. With the transpose done, it is
 loaded from LDS to register again (``%276``), and with a few more
 element-wise operations, it is stored to LDS again (``%298``). The last step
@@ -1967,7 +1607,7 @@ something similar to the following:
   loaded at: [0x7fd4f100c000-0x7fd4f100e070]

 The kernel name and the code object file should be listed. In the
-example above, the kernel name is vector_add_assert_trap, but this might
+example above, the kernel name is vector_add_assert_trap, but this might
 also look like:

 .. code-block:: text
@@ -2081,3 +1721,8 @@ Hardware efficiency is maximized with 4 or fewer HIP streams. These environment
 configuration to two compute streams and two RCCL streams, aligning with this best practice.
 Additionally, RCCL is often pre-optimized for MI300 systems in production by querying the node
 topology during startup, reducing the need for extensive manual tuning.
+
+Further reading
+===============
+
+* :doc:`vllm-optimization`
@@ -0,0 +1,482 @@
+:orphan:
+
+.. meta::
+   :description: Learn how to validate LLM inference performance on MI300X GPUs using AMD MAD and the ROCm vLLM Docker image.
+   :keywords: model, MAD, automation, dashboarding, validate
+
+**********************************
+vLLM inference performance testing
+**********************************
+
+.. caution::
+
+   This documentation does not reflect the latest version of ROCm vLLM
+   inference performance documentation. See :doc:`../vllm` for the latest version.
+
+.. _vllm-benchmark-unified-docker-930:
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.10.1_20251006-benchmark-models.yaml
+
+   {% set docker = data.dockers[0] %}
+
+   The `ROCm vLLM Docker <{{ docker.docker_hub_url }}>`_ image offers a
+   prebuilt, optimized environment for validating large language model (LLM)
+   inference performance on AMD Instinct™ MI355X, MI350X, MI325X and MI300X
+   GPUs. This ROCm vLLM Docker image integrates vLLM and PyTorch tailored
+   specifically for AMD data center GPUs and includes the following components:
+
+   .. tab-set::
+
+      .. tab-item:: {{ docker.pull_tag }}
+
+         .. list-table::
+            :header-rows: 1
+
+            * - Software component
+              - Version
+
+            {% for component_name, component_version in docker.components.items() %}
+            * - {{ component_name }}
+              - {{ component_version }}
+            {% endfor %}
+
+With this Docker image, you can quickly test the :ref:`expected
+inference performance numbers <vllm-benchmark-performance-measurements-930>` for
+AMD Instinct GPUs.
+
+What's new
+==========
+
+The following is summary of notable changes since the :doc:`previous ROCm/vLLM Docker release <vllm-history>`.
+
+* Added support for AMD Instinct MI355X and MI350X GPUs.
+
+* Added support and benchmarking instructions for the following models. See :ref:`vllm-benchmark-supported-models-930`.
+
+  * Llama 4 Scout and Maverick
+
+  * DeepSeek R1 0528 FP8
+
+  * MXFP4 models (MI355X and MI350X only): Llama 3.3 70B MXFP4 and Llama 3.1 405B MXFP4
+
+  * GPT OSS 20B and 120B
+
+  * Qwen 3 32B, 30B-A3B, and 235B-A22B
+
+* Removed the deprecated ``--max-seq-len-to-capture`` flag.
+
+* ``--gpu-memory-utilization`` is now configurable via the `configuration files
+  <https://github.com/ROCm/MAD/tree/develop/scripts/vllm/configs>`__ in the MAD
+  repository.
+
+.. _vllm-benchmark-supported-models-930:
+
+Supported models
+================
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.10.1_20251006-benchmark-models.yaml
+
+   {% set docker = data.dockers[0] %}
+   {% set model_groups = data.model_groups %}
+
+   .. _vllm-benchmark-available-models-930:
+
+   The following models are supported for inference performance benchmarking
+   with vLLM and ROCm. Some instructions, commands, and recommendations in this
+   documentation might vary by model -- select one to get started. MXFP4 models
+   are only supported on MI355X and MI350X GPUs.
+
+   .. raw:: html
+
+      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
+         <div class="row gx-0">
+            <div class="col-2 me-1 px-2 model-param-head">Model</div>
+            <div class="row col-10 pe-0">
+      {% for model_group in model_groups %}
+               <div class="col-4 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
+      {% endfor %}
+            </div>
+         </div>
+
+         <div class="row gx-0 pt-1">
+            <div class="col-2 me-1 px-2 model-param-head">Variant</div>
+            <div class="row col-10 pe-0">
+      {% for model_group in model_groups %}
+         {% set models = model_group.models %}
+         {% for model in models %}
+            {% if models|length % 3 == 0 %}
+               <div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
+            {% else %}
+               <div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
+            {% endif %}
+         {% endfor %}
+      {% endfor %}
+            </div>
+         </div>
+      </div>
+
+   .. _vllm-benchmark-vllm-930:
+
+   {% for model_group in model_groups %}
+      {% for model in model_group.models %}
+
+   .. container:: model-doc {{ model.mad_tag }}
+
+
+      {% if model.precision == "float4" %}
+      .. important::
+
+         MXFP4 is supported only on MI355X and MI350X GPUs.
+      {% endif %}
+
+      .. note::
+
+         See the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`_ to learn more about your selected model.
+         Some models require access authorization prior to use via an external license agreement through a third party.
+      {% if model.precision == "float8" and model.model_repo.startswith("amd") %}
+         This model uses FP8 quantization via `AMD Quark <https://quark.docs.amd.com/latest/>`__ for efficient inference on AMD GPUs.
+      {% endif %}
+      {% if model.precision == "float4" and model.model_repo.startswith("amd") %}
+         This model uses FP4 quantization via `AMD Quark <https://quark.docs.amd.com/latest/>`__ for efficient inference on AMD GPUs.
+      {% endif %}
+
+      {% endfor %}
+   {% endfor %}
+
+.. _vllm-benchmark-performance-measurements-930:
+
+Performance measurements
+========================
+
+To evaluate performance, the
+`Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
+page provides reference throughput and serving measurements for inferencing popular AI models.
+
+.. important::
+
+   The performance data presented in
+   `Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
+   only reflects the latest version of this inference benchmarking environment.
+   The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct GPUs or ROCm software.
+
+System validation
+=================
+
+Before running AI workloads, it's important to validate that your AMD hardware is configured
+correctly and performing optimally.
+
+If you have already validated your system settings, including aspects like NUMA auto-balancing, you
+can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
+optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
+before starting training.
+
+To test for optimal performance, consult the recommended :ref:`System health benchmarks
+<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
+system's configuration.
+
+Pull the Docker image
+=====================
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.10.1_20251006-benchmark-models.yaml
+
+   {% set docker = data.dockers[0] %}
+
+   Download the `ROCm vLLM Docker image <{{ docker.docker_hub_url }}>`_.
+   Use the following command to pull the Docker image from Docker Hub.
+
+   .. code-block:: shell
+
+      docker pull {{ docker.pull_tag }}
+
+Benchmarking
+============
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.10.1_20251006-benchmark-models.yaml
+
+   {% set docker = data.dockers[0] %}
+   {% set model_groups = data.model_groups %}
+
+   Once the setup is complete, choose between two options to reproduce the
+   benchmark results:
+
+   .. _vllm-benchmark-mad-930:
+
+   {% for model_group in model_groups %}
+      {% for model in model_group.models %}
+
+   .. container:: model-doc {{model.mad_tag}}
+
+      .. tab-set::
+
+         .. tab-item:: MAD-integrated benchmarking
+
+            The following run command is tailored to {{ model.model }}.
+            See :ref:`vllm-benchmark-supported-models-930` to switch to another available model.
+
+            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
+               directory and install the required packages on the host machine.
+
+               .. code-block:: shell
+
+                  git clone https://github.com/ROCm/MAD
+                  cd MAD
+                  pip install -r requirements.txt
+
+            2. On the host machine, use this command to run the performance benchmark test on
+               the `{{model.model}} <{{ model.url }}>`_ model using one node with the
+               :literal:`{{model.precision}}` data type.
+
+               .. code-block:: shell
+
+                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
+                  madengine run \
+                      --tags {{model.mad_tag}} \
+                      --keep-model-dir \
+                      --live-output
+
+            MAD launches a Docker container with the name
+            ``container_ci-{{model.mad_tag}}``. The throughput and serving reports of the
+            model are collected in the following paths: ``{{ model.mad_tag }}_throughput.csv``
+            and ``{{ model.mad_tag }}_serving.csv``.
+
+            Although the :ref:`available models
+            <vllm-benchmark-available-models-930>` are preconfigured to collect
+            offline throughput and online serving performance data, you can
+            also change the benchmarking parameters. See the standalone
+            benchmarking tab for more information.
+
+            {% if model.tunableop %}
+
+            .. note::
+
+               For improved performance, consider enabling :ref:`PyTorch TunableOp <mi300x-tunableop>`.
+               TunableOp automatically explores different implementations and configurations of certain PyTorch
+               operators to find the fastest one for your hardware.
+
+               By default, ``{{model.mad_tag}}`` runs with TunableOp disabled (see
+               `<https://github.com/ROCm/MAD/blob/develop/models.json>`__). To enable it, include
+               the ``--tunableop on`` argument in your run.
+
+               Enabling TunableOp triggers a two-pass run -- a warm-up followed by the
+               performance-collection run.
+
+            {% endif %}
+
+         .. tab-item:: Standalone benchmarking
+
+            The following commands are optimized for {{ model.model }}.
+            See :ref:`vllm-benchmark-supported-models-930` to switch to another available model.
+
+            .. seealso::
+
+               For more information on configuration, see the `config files
+               <https://github.com/ROCm/MAD/tree/develop/scripts/vllm/configs>`__
+               in the MAD repository. Refer to the `vLLM engine <https://docs.vllm.ai/en/latest/configuration/engine_args.html#engineargs>`__
+               for descriptions of available configuration options
+               and `Benchmarking vLLM <https://github.com/vllm-project/vllm/blob/main/benchmarks/README.md>`__ for
+               additional benchmarking information.
+
+            .. rubric:: Launch the container
+
+            You can run the vLLM benchmark tool independently by starting the
+            `Docker container <{{ docker.docker_hub_url }}>`_ as shown
+            in the following snippet.
+
+            .. code-block:: shell
+
+               docker pull {{ docker.pull_tag }}
+               docker run -it \
+                   --device=/dev/kfd \
+                   --device=/dev/dri \
+                   --group-add video \
+                   --shm-size 16G \
+                   --security-opt seccomp=unconfined \
+                   --security-opt apparmor=unconfined \
+                   --cap-add=SYS_PTRACE \
+                   -v $(pwd):/workspace \
+                   --env HUGGINGFACE_HUB_CACHE=/workspace \
+                   --name test \
+                   {{ docker.pull_tag }}
+
+            .. rubric:: Throughput command
+
+            Use the following command to start the throughput benchmark.
+
+            .. code-block:: shell
+
+               model={{ model.model_repo }}
+               tp={{ model.config.tp }}
+               num_prompts={{ model.config.num_prompts | default(1024) }}
+               in={{ model.config.in | default(128) }}
+               out={{ model.config.in | default(128) }}
+               dtype={{ model.config.dtype | default("auto") }}
+               kv_cache_dtype={{ model.config.kv_cache_dtype }}
+               max_num_seqs={{ model.config.max_num_seqs | default(1024) }}
+               max_num_batched_tokens={{ model.config.max_num_batched_tokens }}
+               max_model_len={{ model.config.max_model_len }}
+
+               vllm bench throughput --model $model \
+                   -tp $tp \
+                   --num-prompts $num_prompts \
+                   --input-len $in \
+                   --output-len $out \
+                   --dtype $dtype \
+                   --kv-cache-dtype $kv_cache_dtype \
+                   --max-num-seqs $max_num_seqs \
+                   --max-num-batched-tokens $max_num_batched_tokens \
+                   --max-model-len $max_model_len \
+                   --trust-remote-code \
+                   --output-json ${model}_throughput.json \
+                   --gpu-memory-utilization {{ model.config.gpu_memory_utilization | default(0.9) }}
+
+            .. rubric:: Serving command
+
+            1. Start the server using the following command:
+
+               .. code-block:: shell
+
+                  model={{ model.model_repo }}
+                  tp={{ model.config.tp }}
+                  dtype={{ model.config.dtype }}
+                  kv_cache_dtype={{ model.config.kv_cache_dtype }}
+                  max_num_seqs=256
+                  max_num_batched_tokens={{ model.config.max_num_batched_tokens }}
+                  max_model_len={{ model.config.max_model_len }}
+
+                  vllm serve $model \
+                      -tp $tp \
+                      --dtype $dtype \
+                      --kv-cache-dtype $kv_cache_dtype \
+                      --max-num-seqs $max_num_seqs \
+                      --max-num-batched-tokens $max_num_batched_tokens \
+                      --max-model-len $max_model_len \
+                      --no-enable-prefix-caching \
+                      --swap-space 16 \
+                      --disable-log-requests \
+                      --trust-remote-code \
+                      --gpu-memory-utilization 0.9
+
+               Wait until the model has loaded and the server is ready to accept requests.
+
+            2. On another terminal on the same machine, run the benchmark:
+
+               .. code-block:: shell
+
+                  # Connect to the container
+                  docker exec -it test bash
+
+                  # Wait for the server to start
+                  until curl -s http://localhost:8000/v1/models; do sleep 30; done
+
+                  # Run the benchmark
+                  model={{ model.model_repo }}
+                  max_concurrency=1
+                  num_prompts=10
+                  in=128
+                  out=128
+                  vllm bench serve --model $model \
+                      --percentile-metrics "ttft,tpot,itl,e2el" \
+                      --dataset-name random \
+                      --ignore-eos \
+                      --max-concurrency $max_concurrency \
+                      --num-prompts $num_prompts \
+                      --random-input-len $in \
+                      --random-output-len $out \
+                      --trust-remote-code \
+                      --save-result \
+                      --result-filename ${model}_serving.json
+
+            .. note::
+
+               For improved performance with certain Mixture of Experts models, such as Mixtral 8x22B,
+               try adding ``export VLLM_ROCM_USE_AITER=1`` to your commands.
+
+               If you encounter the following error, pass your access-authorized Hugging
+               Face token to the gated models.
+
+               .. code-block::
+
+                  OSError: You are trying to access a gated repo.
+
+                  # pass your HF_TOKEN
+                  export HF_TOKEN=$your_personal_hf_token
+
+            .. raw:: html
+
+               <style>
+               mjx-container[jax="CHTML"][display="true"] {
+                  text-align: left;
+                  margin: 0;
+               }
+               </style>
+
+            .. note::
+
+               Throughput is calculated as:
+
+               - .. math:: throughput\_tot = requests \times (\mathsf{\text{input lengths}} + \mathsf{\text{output lengths}}) / elapsed\_time
+
+               - .. math:: throughput\_gen = requests \times \mathsf{\text{output lengths}} / elapsed\_time
+      {% endfor %}
+   {% endfor %}
+
+Advanced usage
+==============
+
+For information on experimental features and known issues related to ROCm optimization efforts on vLLM,
+see the developer's guide at `<https://github.com/ROCm/vllm/blob/documentation/docs/dev-docker/README.md>`__.
+
+Reproducing the Docker image
+----------------------------
+
+To reproduce this ROCm-enabled vLLM Docker image release, follow these steps:
+
+1. Clone the `vLLM repository <https://github.com/vllm-project/vllm>`__.
+
+   .. code-block:: shell
+
+      git clone https://github.com/vllm-project/vllm.git
+      cd vllm
+
+2. Use the following command to build the image directly from the specified commit.
+
+   .. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.10.1_20251006-benchmark-models.yaml
+
+      {% set docker = data.dockers[0] %}
+      .. code-block:: shell
+
+         docker build -f docker/Dockerfile.rocm \
+             --build-arg REMOTE_VLLM=1 \
+             --build-arg VLLM_REPO=https://github.com/ROCm/vllm \
+             --build-arg VLLM_BRANCH="{{ docker.dockerfile.commit }}" \
+             -t vllm-rocm .
+
+   .. tip::
+
+      Replace ``vllm-rocm`` with your desired image tag.
+
+Further reading
+===============
+
+- To learn more about the options for latency and throughput benchmark scripts,
+  see `<https://github.com/ROCm/vllm/tree/main/benchmarks>`_.
+
+- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
+
+- To learn more about system settings and management practices to configure your system for
+  AMD Instinct MI300X Series GPUs, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
+
+- See :ref:`fine-tuning-llms-vllm` and :ref:`mi300x-vllm-optimization` for
+  a brief introduction to vLLM and optimization strategies.
+
+- For application performance optimization strategies for HPC and AI workloads,
+  including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
+
+- For a list of other ready-made Docker images for AI with ROCm, see
+  `AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
+
+Previous versions
+=================
+
+See :doc:`vllm-history` to find documentation for previous releases
+of the ``ROCm/vllm`` Docker image.
@@ -0,0 +1,472 @@
+:orphan:
+
+.. meta::
+   :description: Learn how to validate LLM inference performance on MI300X GPUs using AMD MAD and the ROCm vLLM Docker image.
+   :keywords: model, MAD, automation, dashboarding, validate
+
+**********************************
+vLLM inference performance testing
+**********************************
+
+.. caution::
+
+   This documentation does not reflect the latest version of ROCm vLLM
+   inference performance documentation. See :doc:`../vllm` for the latest version.
+
+.. _vllm-benchmark-unified-docker-1103:
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.11.1_20251103-benchmark-models.yaml
+
+   {% set docker = data.dockers[0] %}
+
+   The `ROCm vLLM Docker <{{ docker.docker_hub_url }}>`_ image offers a
+   prebuilt, optimized environment for validating large language model (LLM)
+   inference performance on AMD Instinct™ MI355X, MI350X, MI325X and MI300X
+   GPUs. This ROCm vLLM Docker image integrates vLLM and PyTorch tailored
+   specifically for AMD data center GPUs and includes the following components:
+
+   .. tab-set::
+
+      .. tab-item:: {{ docker.pull_tag }}
+
+         .. list-table::
+            :header-rows: 1
+
+            * - Software component
+              - Version
+
+            {% for component_name, component_version in docker.components.items() %}
+            * - {{ component_name }}
+              - {{ component_version }}
+            {% endfor %}
+
+With this Docker image, you can quickly test the :ref:`expected
+inference performance numbers <vllm-benchmark-performance-measurements-1103>` for
+AMD Instinct GPUs.
+
+What's new
+==========
+
+The following is summary of notable changes since the :doc:`previous ROCm/vLLM Docker release <vllm-history>`.
+
+* Enabled :ref:`AITER <vllm-optimization-aiter-switches>` by default.
+
+* Fixed ``rms_norm`` segfault issue with Qwen 3 235B.
+
+* Known performance degradation on Llama 4 models due to `an upstream vLLM issue <https://github.com/vllm-project/vllm/issues/26320>`_.
+
+.. _vllm-benchmark-supported-models-1103:
+
+Supported models
+================
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.11.1_20251103-benchmark-models.yaml
+
+   {% set docker = data.dockers[0] %}
+   {% set model_groups = data.model_groups %}
+
+   .. _vllm-benchmark-available-models-1103:
+
+   The following models are supported for inference performance benchmarking
+   with vLLM and ROCm. Some instructions, commands, and recommendations in this
+   documentation might vary by model -- select one to get started. MXFP4 models
+   are only supported on MI355X and MI350X GPUs.
+
+   .. raw:: html
+
+      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
+         <div class="row gx-0">
+            <div class="col-2 me-1 px-2 model-param-head">Model</div>
+            <div class="row col-10 pe-0">
+      {% for model_group in model_groups %}
+               <div class="col-4 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
+      {% endfor %}
+            </div>
+         </div>
+
+         <div class="row gx-0 pt-1">
+            <div class="col-2 me-1 px-2 model-param-head">Variant</div>
+            <div class="row col-10 pe-0">
+      {% for model_group in model_groups %}
+         {% set models = model_group.models %}
+         {% for model in models %}
+            {% if models|length % 3 == 0 %}
+               <div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
+            {% else %}
+               <div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
+            {% endif %}
+         {% endfor %}
+      {% endfor %}
+            </div>
+         </div>
+      </div>
+
+   .. _vllm-benchmark-vllm-1103:
+
+   {% for model_group in model_groups %}
+      {% for model in model_group.models %}
+
+   .. container:: model-doc {{ model.mad_tag }}
+
+
+      {% if model.precision == "float4" %}
+      .. important::
+
+         MXFP4 is supported only on MI355X and MI350X GPUs.
+      {% endif %}
+
+      .. note::
+
+         See the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`_ to learn more about your selected model.
+         Some models require access authorization prior to use via an external license agreement through a third party.
+      {% if model.precision == "float8" and model.model_repo.startswith("amd") %}
+         This model uses FP8 quantization via `AMD Quark <https://quark.docs.amd.com/latest/>`__ for efficient inference on AMD GPUs.
+      {% endif %}
+      {% if model.precision == "float4" and model.model_repo.startswith("amd") %}
+         This model uses FP4 quantization via `AMD Quark <https://quark.docs.amd.com/latest/>`__ for efficient inference on AMD GPUs.
+      {% endif %}
+
+      {% endfor %}
+   {% endfor %}
+
+.. _vllm-benchmark-performance-measurements-1103:
+
+Performance measurements
+========================
+
+To evaluate performance, the
+`Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
+page provides reference throughput and serving measurements for inferencing popular AI models.
+
+.. important::
+
+   The performance data presented in
+   `Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
+   only reflects the latest version of this inference benchmarking environment.
+   The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct GPUs or ROCm software.
+
+System validation
+=================
+
+Before running AI workloads, it's important to validate that your AMD hardware is configured
+correctly and performing optimally.
+
+If you have already validated your system settings, including aspects like NUMA auto-balancing, you
+can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
+optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
+before starting training.
+
+To test for optimal performance, consult the recommended :ref:`System health benchmarks
+<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
+system's configuration.
+
+Pull the Docker image
+=====================
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.11.1_20251103-benchmark-models.yaml
+
+   {% set docker = data.dockers[0] %}
+
+   Download the `ROCm vLLM Docker image <{{ docker.docker_hub_url }}>`_.
+   Use the following command to pull the Docker image from Docker Hub.
+
+   .. code-block:: shell
+
+      docker pull {{ docker.pull_tag }}
+
+Benchmarking
+============
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.11.1_20251103-benchmark-models.yaml
+
+   {% set docker = data.dockers[0] %}
+   {% set model_groups = data.model_groups %}
+
+   Once the setup is complete, choose between two options to reproduce the
+   benchmark results:
+
+   .. _vllm-benchmark-mad-1103:
+
+   {% for model_group in model_groups %}
+      {% for model in model_group.models %}
+
+   .. container:: model-doc {{model.mad_tag}}
+
+      .. tab-set::
+
+         .. tab-item:: MAD-integrated benchmarking
+
+            The following run command is tailored to {{ model.model }}.
+            See :ref:`vllm-benchmark-supported-models-1103` to switch to another available model.
+
+            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
+               directory and install the required packages on the host machine.
+
+               .. code-block:: shell
+
+                  git clone https://github.com/ROCm/MAD
+                  cd MAD
+                  pip install -r requirements.txt
+
+            2. On the host machine, use this command to run the performance benchmark test on
+               the `{{model.model}} <{{ model.url }}>`_ model using one node with the
+               :literal:`{{model.precision}}` data type.
+
+               .. code-block:: shell
+
+                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
+                  madengine run \
+                      --tags {{model.mad_tag}} \
+                      --keep-model-dir \
+                      --live-output
+
+            MAD launches a Docker container with the name
+            ``container_ci-{{model.mad_tag}}``. The throughput and serving reports of the
+            model are collected in the following paths: ``{{ model.mad_tag }}_throughput.csv``
+            and ``{{ model.mad_tag }}_serving.csv``.
+
+            Although the :ref:`available models
+            <vllm-benchmark-available-models-1103>` are preconfigured to collect
+            offline throughput and online serving performance data, you can
+            also change the benchmarking parameters. See the standalone
+            benchmarking tab for more information.
+
+            {% if model.tunableop %}
+
+            .. note::
+
+               For improved performance, consider enabling :ref:`PyTorch TunableOp <mi300x-tunableop>`.
+               TunableOp automatically explores different implementations and configurations of certain PyTorch
+               operators to find the fastest one for your hardware.
+
+               By default, ``{{model.mad_tag}}`` runs with TunableOp disabled (see
+               `<https://github.com/ROCm/MAD/blob/develop/models.json>`__). To enable it, include
+               the ``--tunableop on`` argument in your run.
+
+               Enabling TunableOp triggers a two-pass run -- a warm-up followed by the
+               performance-collection run.
+
+            {% endif %}
+
+         .. tab-item:: Standalone benchmarking
+
+            The following commands are optimized for {{ model.model }}.
+            See :ref:`vllm-benchmark-supported-models-1103` to switch to another available model.
+
+            .. seealso::
+
+               For more information on configuration, see the `config files
+               <https://github.com/ROCm/MAD/tree/develop/scripts/vllm/configs>`__
+               in the MAD repository. Refer to the `vLLM engine <https://docs.vllm.ai/en/latest/configuration/engine_args.html#engineargs>`__
+               for descriptions of available configuration options
+               and `Benchmarking vLLM <https://github.com/vllm-project/vllm/blob/main/benchmarks/README.md>`__ for
+               additional benchmarking information.
+
+            .. rubric:: Launch the container
+
+            You can run the vLLM benchmark tool independently by starting the
+            `Docker container <{{ docker.docker_hub_url }}>`_ as shown
+            in the following snippet.
+
+            .. code-block:: shell
+
+               docker pull {{ docker.pull_tag }}
+               docker run -it \
+                   --device=/dev/kfd \
+                   --device=/dev/dri \
+                   --group-add video \
+                   --shm-size 16G \
+                   --security-opt seccomp=unconfined \
+                   --security-opt apparmor=unconfined \
+                   --cap-add=SYS_PTRACE \
+                   -v $(pwd):/workspace \
+                   --env HUGGINGFACE_HUB_CACHE=/workspace \
+                   --name test \
+                   {{ docker.pull_tag }}
+
+            .. rubric:: Throughput command
+
+            Use the following command to start the throughput benchmark.
+
+            .. code-block:: shell
+
+               model={{ model.model_repo }}
+               tp={{ model.config.tp }}
+               num_prompts={{ model.config.num_prompts | default(1024) }}
+               in={{ model.config.in | default(128) }}
+               out={{ model.config.in | default(128) }}
+               dtype={{ model.config.dtype | default("auto") }}
+               kv_cache_dtype={{ model.config.kv_cache_dtype }}
+               max_num_seqs={{ model.config.max_num_seqs | default(1024) }}
+               max_num_batched_tokens={{ model.config.max_num_batched_tokens }}
+               max_model_len={{ model.config.max_model_len }}
+
+               vllm bench throughput --model $model \
+                   -tp $tp \
+                   --num-prompts $num_prompts \
+                   --input-len $in \
+                   --output-len $out \
+                   --dtype $dtype \
+                   --kv-cache-dtype $kv_cache_dtype \
+                   --max-num-seqs $max_num_seqs \
+                   --max-num-batched-tokens $max_num_batched_tokens \
+                   --max-model-len $max_model_len \
+                   --trust-remote-code \
+                   --output-json ${model}_throughput.json \
+                   --gpu-memory-utilization {{ model.config.gpu_memory_utilization | default(0.9) }}
+
+            .. rubric:: Serving command
+
+            1. Start the server using the following command:
+
+               .. code-block:: shell
+
+                  model={{ model.model_repo }}
+                  tp={{ model.config.tp }}
+                  dtype={{ model.config.dtype }}
+                  kv_cache_dtype={{ model.config.kv_cache_dtype }}
+                  max_num_seqs=256
+                  max_num_batched_tokens={{ model.config.max_num_batched_tokens }}
+                  max_model_len={{ model.config.max_model_len }}
+
+                  vllm serve $model \
+                      -tp $tp \
+                      --dtype $dtype \
+                      --kv-cache-dtype $kv_cache_dtype \
+                      --max-num-seqs $max_num_seqs \
+                      --max-num-batched-tokens $max_num_batched_tokens \
+                      --max-model-len $max_model_len \
+                      --no-enable-prefix-caching \
+                      --swap-space 16 \
+                      --disable-log-requests \
+                      --trust-remote-code \
+                      --gpu-memory-utilization 0.9
+
+               Wait until the model has loaded and the server is ready to accept requests.
+
+            2. On another terminal on the same machine, run the benchmark:
+
+               .. code-block:: shell
+
+                  # Connect to the container
+                  docker exec -it test bash
+
+                  # Wait for the server to start
+                  until curl -s http://localhost:8000/v1/models; do sleep 30; done
+
+                  # Run the benchmark
+                  model={{ model.model_repo }}
+                  max_concurrency=1
+                  num_prompts=10
+                  in=128
+                  out=128
+                  vllm bench serve --model $model \
+                      --percentile-metrics "ttft,tpot,itl,e2el" \
+                      --dataset-name random \
+                      --ignore-eos \
+                      --max-concurrency $max_concurrency \
+                      --num-prompts $num_prompts \
+                      --random-input-len $in \
+                      --random-output-len $out \
+                      --trust-remote-code \
+                      --save-result \
+                      --result-filename ${model}_serving.json
+
+            .. note::
+
+               For improved performance with certain Mixture of Experts models, such as Mixtral 8x22B,
+               try adding ``export VLLM_ROCM_USE_AITER=1`` to your commands.
+
+               If you encounter the following error, pass your access-authorized Hugging
+               Face token to the gated models.
+
+               .. code-block::
+
+                  OSError: You are trying to access a gated repo.
+
+                  # pass your HF_TOKEN
+                  export HF_TOKEN=$your_personal_hf_token
+
+            .. raw:: html
+
+               <style>
+               mjx-container[jax="CHTML"][display="true"] {
+                  text-align: left;
+                  margin: 0;
+               }
+               </style>
+
+            .. note::
+
+               Throughput is calculated as:
+
+               - .. math:: throughput\_tot = requests \times (\mathsf{\text{input lengths}} + \mathsf{\text{output lengths}}) / elapsed\_time
+
+               - .. math:: throughput\_gen = requests \times \mathsf{\text{output lengths}} / elapsed\_time
+      {% endfor %}
+   {% endfor %}
+
+Advanced usage
+==============
+
+For information on experimental features and known issues related to ROCm optimization efforts on vLLM,
+see the developer's guide at `<https://github.com/ROCm/vllm/blob/documentation/docs/dev-docker/README.md>`__.
+
+.. note::
+
+   If you’re using this Docker image on other AMD GPUs such as the AMD Instinct MI200 Series or Radeon, add ``export VLLM_ROCM_USE_AITER=0`` to your command, since AITER is only supported on gfx942 and gfx950 architectures.
+
+Reproducing the Docker image
+----------------------------
+
+To reproduce this ROCm-enabled vLLM Docker image release, follow these steps:
+
+1. Clone the `vLLM repository <https://github.com/vllm-project/vllm>`__.
+
+   .. code-block:: shell
+
+      git clone https://github.com/vllm-project/vllm.git
+      cd vllm
+
+2. Use the following command to build the image directly from the specified commit.
+
+   .. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.11.1_20251103-benchmark-models.yaml
+
+      {% set docker = data.dockers[0] %}
+      .. code-block:: shell
+
+         docker build -f docker/Dockerfile.rocm \
+             --build-arg REMOTE_VLLM=1 \
+             --build-arg VLLM_REPO=https://github.com/ROCm/vllm \
+             --build-arg VLLM_BRANCH="{{ docker.dockerfile.commit }}" \
+             -t vllm-rocm .
+
+   .. tip::
+
+      Replace ``vllm-rocm`` with your desired image tag.
+
+Further reading
+===============
+
+- To learn more about the options for latency and throughput benchmark scripts,
+  see `<https://github.com/ROCm/vllm/tree/main/benchmarks>`_.
+
+- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
+
+- To learn more about system settings and management practices to configure your system for
+  AMD Instinct MI300X Series GPUs, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
+
+- See :ref:`fine-tuning-llms-vllm` and :ref:`mi300x-vllm-optimization` for
+  a brief introduction to vLLM and optimization strategies.
+
+- For application performance optimization strategies for HPC and AI workloads,
+  including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
+
+- For a list of other ready-made Docker images for AI with ROCm, see
+  `AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
+
+Previous versions
+=================
+
+See :doc:`vllm-history` to find documentation for previous releases
+of the ``ROCm/vllm`` Docker image.
@@ -16,14 +16,31 @@ previous releases of the ``ROCm/vllm`` Docker image on `Docker Hub <https://hub.
     - Components
     - Resources

+   * - ``rocm/vllm:rocm7.0.0_vllm_0.11.2_20251210``
+     -
+       * ROCm 7.0.0
+       * vLLM 0.11.2
+       * PyTorch 2.9.0
+     -
+       * :doc:`Documentation <../vllm>`
+       * `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm7.0.0_vllm_0.11.2_20251210/images/sha256-e7f02dd2ce3824959658bc0391296f6158638e3ebce164f6c019c4eca8150ec7>`__
+
+   * - ``rocm/vllm:rocm7.0.0_vllm_0.11.1_20251103``
+     -
+       * ROCm 7.0.0
+       * vLLM 0.11.1
+       * PyTorch 2.9.0
+     -
+       * :doc:`Documentation <vllm-0.11.1-20251103>`
+       * `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm7.0.0_vllm_0.11.1_20251103/images/sha256-8d60429043d4d00958da46039a1de0d9b82df814d45da482497eef26a6076506>`__
+
   * - ``rocm/vllm:rocm7.0.0_vllm_0.10.2_20251006``
-       (latest)
     -
       * ROCm 7.0.0
       * vLLM 0.10.2
       * PyTorch 2.9.0
     -
-       * :doc:`Documentation <../vllm>`
+       * :doc:`Documentation <vllm-0.10.2-20251006>`
       * `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm7.0.0_vllm_0.10.2_20251006/images/sha256-94fd001964e1cf55c3224a445b1fb5be31a7dac302315255db8422d813edd7f5>`__

   * - ``rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909``
@@ -0,0 +1,398 @@
+:orphan:
+
+.. meta::
+   :description: Learn to validate diffusion model video generation on MI300X, MI350X and MI355X accelerators using
+                 prebuilt and optimized docker images.
+   :keywords: xDiT, diffusion, video, video generation, image, image generation, validate, benchmark
+
+************************
+xDiT diffusion inference
+************************
+
+.. _xdit-video-diffusion-2510:
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.10-inference-models.yaml
+
+   {% set docker = data.xdit_diffusion_inference.docker %}
+   {% set model_groups = data.xdit_diffusion_inference.model_groups%}
+
+   The `rocm/pytorch-xdit <{{ docker.docker_hub_url }}>`_ Docker image offers
+   a prebuilt, optimized inference environment based on `xDiT
+   <https://github.com/xdit-project/xDiT>`_ for benchmarking diffusion-based
+   video and image generation on AMD Instinct MI355X, MI350X (gfx950), MI325X,
+   and MI300X (gfx942) GPUs.
+   This image is based on ROCm {{docker.ROCm}} preview release via `TheRock <https://github.com/ROCm/TheRock>`_
+   and includes the following software components:
+
+   .. tab-set::
+
+      .. tab-item:: {{ docker.pull_tag }}
+
+         .. list-table::
+            :header-rows: 1
+
+            * - Software component
+              - Version
+
+            {% for component_name, component_version in docker.components.items() %}
+            * - {{ component_name }}
+              - {{ component_version }}
+            {% endfor %}
+
+Follow this guide to pull the required image, spin up a container, download the model, and run a benchmark.
+For preview and development releases, see `amdsiloai/pytorch-xdit <https://hub.docker.com/r/amdsiloai/pytorch-xdit>`_.
+
+What's new
+==========
+
+- Initial ROCm-enabled xDiT Docker release for diffusion inference.
+- Supported architectures: gfx942 and gfx950 (AMD Instinct™ MI300X, MI325X, MI350X, and MI355X).
+- Supported workloads: Wan 2.1, Wan 2.2, HunyuanVideo, and Flux models.
+
+.. _xdit-video-diffusion-supported-models-2510:
+
+Supported models
+================
+
+The following models are supported for inference performance benchmarking.
+Some instructions, commands, and recommendations in this documentation might
+vary by model -- select one to get started.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.10-inference-models.yaml
+
+   {% set docker = data.xdit_diffusion_inference.docker %}
+   {% set model_groups = data.xdit_diffusion_inference.model_groups%}
+
+   .. raw:: html
+
+      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
+          <div class="row gx-0">
+              <div class="col-2 me-1 px-2 model-param-head">Model</div>
+              <div class="row col-10 pe-0">
+        {% for model_group in model_groups %}
+                  <div class="col-4 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
+        {% endfor %}
+              </div>
+          </div>
+
+          <div class="row gx-0 pt-1">
+              <div class="col-2 me-1 px-2 model-param-head">Variant</div>
+              <div class="row col-10 pe-0">
+        {% for model_group in model_groups %}
+            {% set models = model_group.models %}
+            {% for model in models %}
+                {% if models|length == 1 %}
+                <div class="col-12 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
+                {% else %}
+                <div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
+                {% endif %}
+            {% endfor %}
+        {% endfor %}
+              </div>
+          </div>
+      </div>
+
+   {% for model_group in model_groups %}
+       {% for model in model_group.models %}
+
+   .. container:: model-doc {{ model.mad_tag }}
+
+      .. note::
+
+         To learn more about your specific model see the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`_
+         or visit the `GitHub page <{{ model.github }}>`__. Note that some models require access authorization before use via an
+         external license agreement through a third party.
+
+       {% endfor %}
+   {% endfor %}
+
+System validation
+=================
+
+Before running AI workloads, it's important to validate that your AMD hardware is configured
+correctly and performing optimally.
+
+If you have already validated your system settings, including aspects like NUMA
+auto-balancing, you can skip this step. Otherwise, complete the procedures in
+the `System validation and optimization
+<https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/system-setup/prerequisite-system-validation.html>`__
+guide to properly configure your system settings before starting.
+
+Pull the Docker image
+=====================
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.10-inference-models.yaml
+
+   {% set docker = data.xdit_diffusion_inference.docker %}
+
+   For this tutorial, it's recommended to use the latest ``{{ docker.pull_tag }}`` Docker image.
+   Pull the image using the following command:
+
+   .. code-block:: shell
+
+      docker pull {{ docker.pull_tag }}
+
+Validate and benchmark
+======================
+
+Once the image has been downloaded you can follow these steps to
+run benchmarks and generate outputs.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.10-inference-models.yaml
+
+   {% set model_groups = data.xdit_diffusion_inference.model_groups %}
+   {% for model_group in model_groups %}
+     {% for model in model_group.models %}
+
+   .. container:: model-doc {{model.mad_tag}}
+
+      The following commands are written for {{ model.model }}.
+      See :ref:`xdit-video-diffusion-supported-models-2510` to switch to another available model.
+
+     {% endfor %}
+   {% endfor %}
+
+.. _xdit-video-diffusion-setup-2510:
+
+Prepare the model
+-----------------
+
+.. note::
+
+   If you're using ROCm MAD to :ref:`run your model
+   <xdit-video-diffusion-run-2510>`, you can skip this section. MAD will handle
+   starting the container and downloading required models inside the container.
+
+You can either use an existing Hugging Face cache or download the model fresh inside the container.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.10-inference-models.yaml
+
+   {% set docker = data.xdit_diffusion_inference.docker %}
+   {% set model_groups = data.xdit_diffusion_inference.model_groups%}
+
+   {% for model_group in model_groups %}
+     {% for model in model_group.models %}
+   .. container:: model-doc {{model.mad_tag}}
+
+      .. tab-set::
+
+         .. tab-item:: Option 1: Use existing Hugging Face cache
+
+            If you already have models downloaded on your host system, you can mount your existing cache.
+
+            1. Set your Hugging Face cache location.
+
+               .. code-block:: shell
+
+                  export HF_HOME=/your/hf_cache/location
+
+            2. Download the model (if not already cached).
+
+               .. code-block:: shell
+
+                  huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
+
+            3. Launch the container with mounted cache.
+
+               .. code-block:: shell
+
+                  docker run \
+                      -it --rm \
+                      --cap-add=SYS_PTRACE \
+                      --security-opt seccomp=unconfined \
+                      --user root \
+                      --device=/dev/kfd \
+                      --device=/dev/dri \
+                      --group-add video \
+                      --ipc=host \
+                      --network host \
+                      --privileged \
+                      --shm-size 128G \
+                      --name pytorch-xdit \
+                      -e HSA_NO_SCRATCH_RECLAIM=1 \
+                      -e OMP_NUM_THREADS=16 \
+                      -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+                      -e HF_HOME=/app/huggingface_models \
+                      -v $HF_HOME:/app/huggingface_models \
+                      {{ docker.pull_tag }}
+
+         .. tab-item:: Option 2: Download inside container
+
+            If you prefer to keep the container self-contained or don't have an existing cache.
+
+            1. Launch the container
+
+               .. code-block:: shell
+
+                  docker run \
+                      -it --rm \
+                      --cap-add=SYS_PTRACE \
+                      --security-opt seccomp=unconfined \
+                      --user root \
+                      --device=/dev/kfd \
+                      --device=/dev/dri \
+                      --group-add video \
+                      --ipc=host \
+                      --network host \
+                      --privileged \
+                      --shm-size 128G \
+                      --name pytorch-xdit \
+                      -e HSA_NO_SCRATCH_RECLAIM=1 \
+                      -e OMP_NUM_THREADS=16 \
+                      -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+                      {{ docker.pull_tag }}
+
+            2. Inside the container, set the Hugging Face cache location and download the model.
+
+               .. code-block:: shell
+
+                  export HF_HOME=/app/huggingface_models
+                  huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
+
+               .. warning::
+
+                  Models will be downloaded to the container's filesystem and will be lost when the container is removed unless you persist the data with a volume.
+     {% endfor %}
+   {% endfor %}
+
+.. _xdit-video-diffusion-run-2510:
+
+Run inference
+=============
+
+You can benchmark models through `MAD <https://github.com/ROCm/MAD>`__-integrated automation or standalone
+torchrun commands.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.10-inference-models.yaml
+
+   {% set model_groups = data.xdit_diffusion_inference.model_groups%}
+   {% for model_group in model_groups %}
+     {% for model in model_group.models %}
+
+   .. container:: model-doc {{ model.mad_tag }}
+
+      .. tab-set::
+
+         .. tab-item:: MAD-integrated benchmarking
+
+            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
+               directory and install the required packages on the host machine.
+
+               .. code-block:: shell
+
+                  git clone https://github.com/ROCm/MAD
+                  cd MAD
+                  pip install -r requirements.txt
+
+            2. On the host machine, use this command to run the performance benchmark test on
+               the `{{model.model}} <{{ model.url }}>`_ model using one node.
+
+               .. code-block:: shell
+
+                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
+                  madengine run \
+                      --tags {{model.mad_tag}} \
+                      --keep-model-dir \
+                      --live-output
+                     
+            MAD launches a Docker container with the name
+            ``container_ci-{{model.mad_tag}}``. The throughput and serving reports of the
+            model are collected in the following paths: ``{{ model.mad_tag }}_throughput.csv``
+            and ``{{ model.mad_tag }}_serving.csv``.
+
+         .. tab-item:: Standalone benchmarking
+
+            To run the benchmarks for {{ model.model }}, use the following command:
+
+            .. code-block:: shell
+            {% if model.model == "Hunyuan Video" %}
+               cd /app/Hunyuanvideo
+               mkdir results
+
+               torchrun --nproc_per_node=8 run.py \
+                  --model tencent/HunyuanVideo \
+                  --prompt "In the large cage, two puppies were wagging their tails at each other." \
+                  --height 720 --width 1280 --num_frames 129 \
+                  --num_inference_steps 50 --warmup_steps 1 --n_repeats 1 \
+                  --ulysses_degree 8 \
+                  --enable_tiling --enable_slicing \
+                  --use_torch_compile \
+                  --bench_output results
+            {% endif %}
+            {% if model.model == "Wan2.1" %}
+               cd Wan2.1
+               mkdir results
+
+               torchrun --nproc_per_node=8 run.py \
+                  --task i2v-14B \
+                  --size 720*1280 --frame_num 81 \
+                  --ckpt_dir "${HF_HOME}/hub/models--Wan-AI--Wan2.1-I2V-14B-720P/snapshots/8823af45fcc58a8aa999a54b04be9abc7d2aac98/" \
+                  --image "/app/Wan2.1/examples/i2v_input.JPG" \
+                  --ulysses_size 8 --ring_size 1 \
+                  --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \
+                  --benchmark_output_directory results --save_file video.mp4 --num_benchmark_steps 1 \
+                  --offload_model 0 \
+                  --vae_dtype bfloat16 \
+                  --allow_tf32 \
+                  --compile
+            {% endif %}
+            {% if model.model == "Wan2.2" %}
+               cd Wan2.2
+               mkdir results
+
+               torchrun --nproc_per_node=8 run.py \
+                  --task i2v-A14B \
+                  --size 720*1280 --frame_num 81 \
+                  --ckpt_dir "${HF_HOME}/hub/models--Wan-AI--Wan2.2-I2V-A14B/snapshots/206a9ee1b7bfaaf8f7e4d81335650533490646a3/" \
+                  --image "/app/Wan2.2/examples/i2v_input.JPG" \
+                  --ulysses_size 8 --ring_size 1 \
+                  --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \
+                  --benchmark_output_directory results --save_file video.mp4 --num_benchmark_steps 1 \
+                  --offload_model 0 \
+                  --vae_dtype bfloat16 \
+                  --allow_tf32 \
+                  --compile
+            {% endif %}
+
+            {% if model.model == "FLUX.1" %}
+               cd Flux
+               mkdir results
+
+               torchrun --nproc_per_node=8 /app/Flux/run.py \
+                  --model black-forest-labs/FLUX.1-dev \
+                  --seed 42 \
+                  --prompt "A small cat" \
+                  --height 1024 \
+                  --width 1024 \
+                  --num_inference_steps 25 \
+                  --max_sequence_length 256 \
+                  --warmup_steps 5 \
+                  --no_use_resolution_binning \
+                  --ulysses_degree 8 \
+                  --use_torch_compile \
+                  --num_repetitions 1 \
+                  --benchmark_output_directory results
+
+            {% endif %}
+
+            The generated video will be stored under the results directory. For the actual benchmark step runtimes, see {% if model.model == "Hunyuan Video" %}stdout.{% elif model.model in ["Wan2.1", "Wan2.2"] %}results/outputs/rank0_*.json{% elif model.model == "FLUX.1" %}results/timing.json{% endif %}
+
+            {% if model.model == "FLUX.1" %}You may also use ``run_usp.py`` which implements USP without modifying the default diffusers pipeline. {% endif %}
+
+      {% endfor %}
+    {% endfor %}
+
+Further reading
+===============
+
+- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
+
+- For a list of other ready-made Docker images for AI with ROCm, see `AMD
+  Infinity Hub
+  <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`__.
+
+Previous versions
+=================
+
+See :doc:`xdit-history` to find documentation for previous releases
+of xDiT diffusion inference performance testing.
@@ -0,0 +1,389 @@
+:orphan:
+
+.. meta::
+   :description: Learn to validate diffusion model video generation on MI300X, MI350X and MI355X accelerators using
+                 prebuilt and optimized docker images.
+   :keywords: xDiT, diffusion, video, video generation, image, image generation, validate, benchmark
+
+************************
+xDiT diffusion inference
+************************
+
+.. caution::
+
+   This documentation does not reflect the latest version of ROCm vLLM
+   inference performance documentation. See
+   :doc:`/how-to/rocm-for-ai/inference/xdit-diffusion-inference` for the latest
+   version.
+
+.. _xdit-video-diffusion-2511:
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.11-inference-models.yaml
+
+   {% set docker = data.xdit_diffusion_inference.docker | selectattr("version", "equalto", "v25-11") | first %}
+   {% set model_groups = data.xdit_diffusion_inference.model_groups%}
+
+   The `rocm/pytorch-xdit <{{ docker.docker_hub_url }}>`_ Docker image offers a prebuilt, optimized environment based on `xDiT <https://github.com/xdit-project/xDiT>`_ for
+   benchmarking diffusion model video and image generation on gfx942 and gfx950 series (AMD Instinct™ MI300X, MI325X, MI350X, and MI355X) GPUs.
+   The image runs ROCm **{{docker.ROCm}}** (preview) based on `TheRock <https://github.com/ROCm/TheRock>`_
+   and includes the following components:
+
+   .. dropdown:: Software components
+
+      .. list-table::
+         :header-rows: 1
+
+         * - Software component
+           - Version
+
+         {% for component_name, component_version in docker.components.items() %}
+         * - {{ component_name }}
+           - {{ component_version }}
+         {% endfor %}
+
+Follow this guide to pull the required image, spin up a container, download the model, and run a benchmark.
+For preview and development releases, see `amdsiloai/pytorch-xdit <https://hub.docker.com/r/amdsiloai/pytorch-xdit>`_.
+
+What's new
+==========
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.11-inference-models.yaml
+
+   {% set docker = data.xdit_diffusion_inference.docker | selectattr("version", "equalto", "v25-11") | first %}
+   {% set model_groups = data.xdit_diffusion_inference.model_groups%}
+
+   {% for item in docker.whats_new %}
+   * {{ item }}
+   {% endfor %}
+
+.. _xdit-video-diffusion-supported-models-2511:
+
+Supported models
+================
+
+The following models are supported for inference performance benchmarking.
+Some instructions, commands, and recommendations in this documentation might
+vary by model -- select one to get started.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.11-inference-models.yaml
+
+   {% set docker = data.xdit_diffusion_inference.docker | selectattr("version", "equalto", "v25-11") | first %}
+   {% set model_groups = data.xdit_diffusion_inference.model_groups %}
+
+   {# Create a lookup for supported models #}
+   {% set supported_lookup = {} %}
+   {% for supported in docker.supported_models %}
+   {% set _ = supported_lookup.update({supported.group: supported.models}) %}
+   {% endfor %}
+
+   .. raw:: html
+
+      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
+          <div class="row gx-0">
+              <div class="col-2 me-1 px-2 model-param-head">Model</div>
+              <div class="row col-10 pe-0">
+        {% for model_group in model_groups %}
+            {% if model_group.group in supported_lookup %}
+                  <div class="col-4 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
+            {% endif %}
+        {% endfor %}
+              </div>
+          </div>
+
+          <div class="row gx-0 pt-1">
+              <div class="col-2 me-1 px-2 model-param-head">Variant</div>
+              <div class="row col-10 pe-0">
+        {% for model_group in model_groups %}
+            {% if model_group.group in supported_lookup %}
+            {% set supported_models = supported_lookup[model_group.group] %}
+            {% set models = model_group.models %}
+            {% for model in models %}
+                {% if model.model in supported_models %}
+                {% if models|length % 3 == 0 %}
+                <div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.page_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
+                {% else %}
+                <div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.page_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
+                {% endif %}
+                {% endif %}
+            {% endfor %}
+            {% endif %}
+        {% endfor %}
+              </div>
+          </div>
+      </div>
+
+   {% for model_group in model_groups %}
+       {% for model in model_group.models %}
+
+   .. container:: model-doc {{ model.page_tag }}
+
+      .. note::
+
+         To learn more about your specific model see the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`_
+         or visit the `GitHub page <{{ model.github }}>`__. Note that some models require access authorization before use via an
+         external license agreement through a third party.
+
+       {% endfor %}
+   {% endfor %}
+
+System validation
+=================
+
+Before running AI workloads, it's important to validate that your AMD hardware is configured
+correctly and performing optimally.
+
+If you have already validated your system settings, including aspects like NUMA auto-balancing, you
+can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
+optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
+before starting.
+
+To test for optimal performance, consult the recommended :ref:`System health benchmarks
+<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
+system's configuration.
+
+Pull the Docker image
+=====================
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.11-inference-models.yaml
+
+   {% set docker = data.xdit_diffusion_inference.docker | selectattr("version", "equalto", "v25-11") | first %}
+
+   For this tutorial, it's recommended to use the latest ``{{ docker.pull_tag }}`` Docker image.
+   Pull the image using the following command:
+
+   .. code-block:: shell
+
+      docker pull {{ docker.pull_tag }}
+
+Validate and benchmark
+======================
+
+Once the image has been downloaded you can follow these steps to
+run benchmarks and generate outputs.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.11-inference-models.yaml
+
+   {% for model_group in model_groups %}
+     {% for model in model_group.models %}
+
+   .. container:: model-doc {{model.page_tag}}
+
+      The following commands are written for {{ model.model }}.
+      See :ref:`xdit-video-diffusion-supported-models-2511` to switch to another available model.
+
+     {% endfor %}
+   {% endfor %}
+
+Choose your setup method
+------------------------
+
+You can either use an existing Hugging Face cache or download the model fresh inside the container.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.11-inference-models.yaml
+
+   {% set docker = data.xdit_diffusion_inference.docker | selectattr("version", "equalto", "v25-11") | first %}
+   {% set model_groups = data.xdit_diffusion_inference.model_groups%}
+
+   {% for model_group in model_groups %}
+     {% for model in model_group.models %}
+
+   .. container:: model-doc {{model.page_tag}}
+
+      .. tab-set::
+
+         .. tab-item:: Option 1: Use existing Hugging Face cache
+
+            If you already have models downloaded on your host system, you can mount your existing cache.
+
+            1. Set your Hugging Face cache location.
+
+               .. code-block:: shell
+
+                  export HF_HOME=/your/hf_cache/location
+            2. Download the model (if not already cached).
+
+               .. code-block:: shell
+
+                  huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
+            3. Launch the container with mounted cache.
+
+               .. code-block:: shell
+
+                  docker run \
+                      -it --rm \
+                      --cap-add=SYS_PTRACE \
+                      --security-opt seccomp=unconfined \
+                      --user root \
+                      --device=/dev/kfd \
+                      --device=/dev/dri \
+                      --group-add video \
+                      --ipc=host \
+                      --network host \
+                      --privileged \
+                      --shm-size 128G \
+                      --name pytorch-xdit \
+                      -e HSA_NO_SCRATCH_RECLAIM=1 \
+                      -e OMP_NUM_THREADS=16 \
+                      -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+                      -e HF_HOME=/app/huggingface_models \
+                      -v $HF_HOME:/app/huggingface_models \
+                      {{ docker.pull_tag }}
+         .. tab-item:: Option 2: Download inside container
+
+            If you prefer to keep the container self-contained or don't have an existing cache.
+
+            1. Launch the container
+
+               .. code-block:: shell
+
+                  docker run \
+                      -it --rm \
+                      --cap-add=SYS_PTRACE \
+                      --security-opt seccomp=unconfined \
+                      --user root \
+                      --device=/dev/kfd \
+                      --device=/dev/dri \
+                      --group-add video \
+                      --ipc=host \
+                      --network host \
+                      --privileged \
+                      --shm-size 128G \
+                      --name pytorch-xdit \
+                      -e HSA_NO_SCRATCH_RECLAIM=1 \
+                      -e OMP_NUM_THREADS=16 \
+                      -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+                      {{ docker.pull_tag }}
+            2. Inside the container, set the Hugging Face cache location and download the model.
+
+               .. code-block:: shell
+
+                  export HF_HOME=/app/huggingface_models
+                  huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
+
+               .. warning::
+
+                  Models will be downloaded to the container's filesystem and will be lost when the container is removed unless you persist the data with a volume.
+     {% endfor %}
+   {% endfor %}
+
+Run inference
+=============
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.11-inference-models.yaml
+
+   {% set model_groups = data.xdit_diffusion_inference.model_groups%}
+   {% for model_group in model_groups %}
+     {% for model in model_group.models %}
+
+   .. container:: model-doc {{ model.page_tag }}
+
+      .. tab-set::
+
+         .. tab-item:: MAD-integrated benchmarking
+
+            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
+               directory and install the required packages on the host machine.
+
+               .. code-block:: shell
+
+                  git clone https://github.com/ROCm/MAD
+                  cd MAD
+                  pip install -r requirements.txt
+
+            2. On the host machine, use this command to run the performance benchmark test on
+               the `{{model.model}} <{{ model.url }}>`_ model using one node.
+
+               .. code-block:: shell
+
+                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
+                  madengine run \
+                      --tags {{model.mad_tag}} \
+                      --keep-model-dir \
+                      --live-output
+
+            MAD launches a Docker container with the name
+            ``container_ci-{{model.mad_tag}}``. The throughput and serving reports of the
+            model are collected in the following paths: ``{{ model.mad_tag }}_throughput.csv``
+            and ``{{ model.mad_tag }}_serving.csv``.
+
+         .. tab-item:: Standalone benchmarking
+
+            To run the benchmarks for {{ model.model }}, use the following command:
+
+            .. code-block:: shell
+
+            {% if model.model == "Hunyuan Video" %}
+               cd /app/Hunyuanvideo
+               mkdir results
+               torchrun --nproc_per_node=8 run.py \
+                  --model tencent/HunyuanVideo \
+                  --prompt "In the large cage, two puppies were wagging their tails at each other." \
+                  --height 720 --width 1280 --num_frames 129 \
+                  --num_inference_steps 50 --warmup_steps 1 --n_repeats 1 \
+                  --ulysses_degree 8 \
+                  --enable_tiling --enable_slicing \
+                  --use_torch_compile \
+                  --bench_output results
+            {% endif %}
+            {% if model.model == "Wan2.1" %}
+               cd Wan2.1
+               mkdir results
+               torchrun --nproc_per_node=8 run.py \
+                  --task i2v-14B \
+                  --size 720*1280 --frame_num 81 \
+                  --ckpt_dir "${HF_HOME}/hub/models--Wan-AI--Wan2.1-I2V-14B-720P/snapshots/8823af45fcc58a8aa999a54b04be9abc7d2aac98/" \
+                  --image "/app/Wan2.1/examples/i2v_input.JPG" \
+                  --ulysses_size 8 --ring_size 1 \
+                  --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \
+                  --benchmark_output_directory results --save_file video.mp4 --num_benchmark_steps 1 \
+                  --offload_model 0 \
+                  --vae_dtype bfloat16 \
+                  --allow_tf32 \
+                  --compile
+            {% endif %}
+            {% if model.model == "Wan2.2" %}
+               cd Wan2.2
+               mkdir results
+               torchrun --nproc_per_node=8 run.py \
+                  --task i2v-A14B \
+                  --size 720*1280 --frame_num 81 \
+                  --ckpt_dir "${HF_HOME}/hub/models--Wan-AI--Wan2.2-I2V-A14B/snapshots/206a9ee1b7bfaaf8f7e4d81335650533490646a3/" \
+                  --image "/app/Wan2.2/examples/i2v_input.JPG" \
+                  --ulysses_size 8 --ring_size 1 \
+                  --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \
+                  --benchmark_output_directory results --save_file video.mp4 --num_benchmark_steps 1 \
+                  --offload_model 0 \
+                  --vae_dtype bfloat16 \
+                  --allow_tf32 \
+                  --compile
+            {% endif %}
+            {% if model.model == "FLUX.1" %}
+               cd Flux
+               mkdir results
+               torchrun --nproc_per_node=8 /app/Flux/run.py \
+                  --model black-forest-labs/FLUX.1-dev \
+                  --seed 42 \
+                  --prompt "A small cat" \
+                  --height 1024 \
+                  --width 1024 \
+                  --num_inference_steps 25 \
+                  --max_sequence_length 256 \
+                  --warmup_steps 5 \
+                  --no_use_resolution_binning \
+                  --ulysses_degree 8 \
+                  --use_torch_compile \
+                  --num_repetitions 1 \
+                  --benchmark_output_directory results
+            {% endif %}
+            The generated video will be stored under the results directory. For the actual benchmark step runtimes, see {% if model.model == "Hunyuan Video" %}stdout.{% elif model.model in ["Wan2.1", "Wan2.2"] %}results/outputs/rank0_*.json{% elif model.model == "FLUX.1" %}results/timing.json{% endif %}
+            {% if model.model == "FLUX.1" %}You may also use ``run_usp.py`` which implements USP without modifying the default diffusers pipeline. {% endif %}
+      {% endfor %}
+    {% endfor %}
+
+Previous versions
+=================
+
+See
+:doc:`/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-history`
+to find documentation for previous releases of xDiT diffusion inference
+performance testing.
@@ -0,0 +1,411 @@
+:orphan:
+
+.. meta::
+   :description: Learn to validate diffusion model video generation on MI300X, MI350X and MI355X accelerators using
+                 prebuilt and optimized docker images.
+   :keywords: xDiT, diffusion, video, video generation, image, image generation, validate, benchmark
+
+************************
+xDiT diffusion inference
+************************
+
+.. caution::
+
+   This documentation does not reflect the latest version of xDiT diffusion
+   inference performance documentation. See
+   :doc:`/how-to/rocm-for-ai/inference/xdit-diffusion-inference` for the latest
+   version.
+
+.. _xdit-video-diffusion-2512:
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.12-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   The `rocm/pytorch-xdit <{{ docker.docker_hub_url }}>`_ Docker image offers
+   a prebuilt, optimized environment based on `xDiT
+   <https://github.com/xdit-project/xDiT>`_ for benchmarking diffusion model
+   video and image generation on AMD Instinct MI355X, MI350X (gfx950), MI325X,
+   and MI300X (gfx942) GPUs.
+
+   The image runs ROCm **{{docker.ROCm}}** (preview) based on `TheRock <https://github.com/ROCm/TheRock>`_
+   and includes the following components:
+
+   .. dropdown:: Software components
+
+      .. list-table::
+         :header-rows: 1
+
+         * - Software component
+           - Version
+
+         {% for component_name, component_data in docker.components.items() %}
+         * - `{{ component_name }} <{{ component_data.url }}>`_
+           - {{ component_data.version }}
+         {% endfor %}
+
+Follow this guide to pull the required image, spin up a container, download the model, and run a benchmark.
+For preview and development releases, see `amdsiloai/pytorch-xdit <https://hub.docker.com/r/amdsiloai/pytorch-xdit>`_.
+
+What's new
+==========
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.12-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   {% for item in docker.whats_new %}
+   * {{ item }}
+   {% endfor %}
+
+.. _xdit-video-diffusion-supported-models-2512:
+
+Supported models
+================
+
+The following models are supported for inference performance benchmarking.
+Some instructions, commands, and recommendations in this documentation might
+vary by model -- select one to get started.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.12-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   .. raw:: html
+
+      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
+          <div class="row gx-0">
+              <div class="col-2 me-1 px-2 model-param-head">Model</div>
+              <div class="row col-10 pe-0">
+        {% for model_group in docker.supported_models %}
+               <div class="col-6 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.js_tag }}" tabindex="0">{{ model_group.group }}</div>
+        {% endfor %}
+              </div>
+          </div>
+
+          <div class="row gx-0 pt-1">
+              <div class="col-2 me-1 px-2 model-param-head">Variant</div>
+              <div class="row col-10 pe-0">
+        {% for model_group in docker.supported_models %}
+            {% set models = model_group.models %}
+            {% for model in models %}
+                {% if models|length % 3 == 0 %}
+                <div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.js_tag }}" data-param-group="{{ model_group.js_tag }}" tabindex="0">{{ model.model }}</div>
+                {% else %}
+                <div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.js_tag }}" data-param-group="{{ model_group.js_tag }}" tabindex="0">{{ model.model }}</div>
+                {% endif %}
+            {% endfor %}
+        {% endfor %}
+              </div>
+          </div>
+      </div>
+
+   {% for model_group in docker.supported_models %}
+       {% for model in model_group.models %}
+
+   .. container:: model-doc {{ model.js_tag }}
+
+      .. note::
+
+         To learn more about your specific model see the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`_
+         or visit the `GitHub page <{{ model.github }}>`__. Note that some models require access authorization before use via an
+         external license agreement through a third party.
+
+       {% endfor %}
+   {% endfor %}
+
+System validation
+=================
+
+Before running AI workloads, it's important to validate that your AMD hardware is configured
+correctly and performing optimally.
+
+If you have already validated your system settings, including aspects like NUMA auto-balancing, you
+can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
+optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
+before starting.
+
+To test for optimal performance, consult the recommended :ref:`System health benchmarks
+<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
+system's configuration.
+
+Pull the Docker image
+=====================
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.12-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   For this tutorial, it's recommended to use the latest ``{{ docker.pull_tag }}`` Docker image.
+   Pull the image using the following command:
+
+   .. code-block:: shell
+
+      docker pull {{ docker.pull_tag }}
+
+Validate and benchmark
+======================
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.12-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   Once the image has been downloaded you can follow these steps to
+   run benchmarks and generate outputs.
+
+   {% for model_group in docker.supported_models %}
+     {% for model in model_group.models %}
+
+   .. container:: model-doc {{model.js_tag}}
+
+      The following commands are written for {{ model.model }}.
+      See :ref:`xdit-video-diffusion-supported-models` to switch to another available model.
+
+     {% endfor %}
+   {% endfor %}
+
+Choose your setup method
+------------------------
+
+You can either use an existing Hugging Face cache or download the model fresh inside the container.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.12-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   {% for model_group in docker.supported_models %}
+     {% for model in model_group.models %}
+   .. container:: model-doc {{model.js_tag}}
+
+      .. tab-set::
+
+         .. tab-item:: Option 1: Use existing Hugging Face cache
+
+            If you already have models downloaded on your host system, you can mount your existing cache.
+
+            1. Set your Hugging Face cache location.
+
+               .. code-block:: shell
+
+                  export HF_HOME=/your/hf_cache/location
+
+            2. Download the model (if not already cached).
+
+               .. code-block:: shell
+
+                  huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
+
+            3. Launch the container with mounted cache.
+
+               .. code-block:: shell
+
+                  docker run \
+                      -it --rm \
+                      --cap-add=SYS_PTRACE \
+                      --security-opt seccomp=unconfined \
+                      --user root \
+                      --device=/dev/kfd \
+                      --device=/dev/dri \
+                      --group-add video \
+                      --ipc=host \
+                      --network host \
+                      --privileged \
+                      --shm-size 128G \
+                      --name pytorch-xdit \
+                      -e HSA_NO_SCRATCH_RECLAIM=1 \
+                      -e OMP_NUM_THREADS=16 \
+                      -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+                      -e HF_HOME=/app/huggingface_models \
+                      -v $HF_HOME:/app/huggingface_models \
+                      {{ docker.pull_tag }}
+
+         .. tab-item:: Option 2: Download inside container
+
+            If you prefer to keep the container self-contained or don't have an existing cache.
+
+            1. Launch the container
+
+               .. code-block:: shell
+
+                  docker run \
+                      -it --rm \
+                      --cap-add=SYS_PTRACE \
+                      --security-opt seccomp=unconfined \
+                      --user root \
+                      --device=/dev/kfd \
+                      --device=/dev/dri \
+                      --group-add video \
+                      --ipc=host \
+                      --network host \
+                      --privileged \
+                      --shm-size 128G \
+                      --name pytorch-xdit \
+                      -e HSA_NO_SCRATCH_RECLAIM=1 \
+                      -e OMP_NUM_THREADS=16 \
+                      -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+                      {{ docker.pull_tag }}
+
+            2. Inside the container, set the Hugging Face cache location and download the model.
+
+               .. code-block:: shell
+
+                  export HF_HOME=/app/huggingface_models
+                  huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
+
+               .. warning::
+
+                  Models will be downloaded to the container's filesystem and will be lost when the container is removed unless you persist the data with a volume.
+     {% endfor %}
+   {% endfor %}
+
+Run inference
+=============
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.12-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   {% for model_group in docker.supported_models %}
+     {% for model in model_group.models %}
+
+   .. container:: model-doc {{ model.js_tag }}
+
+      .. tab-set::
+
+         .. tab-item:: MAD-integrated benchmarking
+
+            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
+               directory and install the required packages on the host machine.
+
+               .. code-block:: shell
+
+                  git clone https://github.com/ROCm/MAD
+                  cd MAD
+                  pip install -r requirements.txt
+
+            2. On the host machine, use this command to run the performance benchmark test on
+               the `{{model.model}} <{{ model.url }}>`_ model using one node.
+
+               .. code-block:: shell
+
+                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
+                  madengine run \
+                      --tags {{model.mad_tag}} \
+                      --keep-model-dir \
+                      --live-output
+
+            MAD launches a Docker container with the name
+            ``container_ci-{{model.mad_tag}}``. The throughput and serving reports of the
+            model are collected in the following paths: ``{{ model.mad_tag }}_throughput.csv``
+            and ``{{ model.mad_tag }}_serving.csv``.
+
+         .. tab-item:: Standalone benchmarking
+
+            To run the benchmarks for {{ model.model }}, use the following command:
+
+            .. code-block:: shell
+            {% if model.model == "Hunyuan Video" %}
+               cd /app/Hunyuanvideo
+               mkdir results
+
+               torchrun --nproc_per_node=8 run.py \
+                  --model {{ model.model_repo }} \
+                  --prompt "In the large cage, two puppies were wagging their tails at each other." \
+                  --height 720 --width 1280 --num_frames 129 \
+                  --num_inference_steps 50 --warmup_steps 1 --n_repeats 1 \
+                  --ulysses_degree 8 \
+                  --enable_tiling --enable_slicing \
+                  --use_torch_compile \
+                  --bench_output results
+
+            {% endif %}
+            {% if model.model == "Wan2.1" %}
+               cd Wan
+               mkdir results
+
+               torchrun --nproc_per_node=8 /app/Wan/run.py \
+                  --task i2v \
+                  --height 720 \
+                  --width 1280 \
+                  --model {{ model.model_repo }} \
+                  --img_file_path /app/Wan/i2v_input.JPG \
+                  --ulysses_degree 8 \
+                  --seed 42 \
+                  --num_frames 81 \
+                  --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \
+                  --num_repetitions 1 \
+                  --num_inference_steps 40 \
+                  --use_torch_compile
+
+            {% endif %}
+            {% if model.model == "Wan2.2" %}
+               cd Wan
+               mkdir results
+
+               torchrun --nproc_per_node=8 /app/Wan/run.py \
+                  --task i2v \
+                  --height 720 \
+                  --width 1280 \
+                  --model {{ model.model_repo }} \
+                  --img_file_path /app/Wan/i2v_input.JPG \
+                  --ulysses_degree 8 \
+                  --seed 42 \
+                  --num_frames 81 \
+                  --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \
+                  --num_repetitions 1 \
+                  --num_inference_steps 40 \
+                  --use_torch_compile
+
+            {% endif %}
+
+            {% if model.model == "FLUX.1" %}
+               cd Flux
+               mkdir results
+
+               torchrun --nproc_per_node=8 /app/Flux/run.py \
+                  --model {{ model.model_repo }} \
+                  --seed 42 \
+                  --prompt "A small cat" \
+                  --height 1024 \
+                  --width 1024 \
+                  --num_inference_steps 25 \
+                  --max_sequence_length 256 \
+                  --warmup_steps 5 \
+                  --no_use_resolution_binning \
+                  --ulysses_degree 8 \
+                  --use_torch_compile \
+                  --num_repetitions 50
+
+            {% endif %}
+
+            {% if model.model == "stable-diffusion-3.5-large" %}
+               cd StableDiffusion3.5
+               mkdir results
+
+               torchrun --nproc_per_node=8 /app/StableDiffusion3.5/run.py \
+                  --model {{ model.model_repo }} \
+                  --num_inference_steps 28 \
+                  --prompt "A capybara holding a sign that reads Hello World" \
+                  --use_torch_compile \
+                  --pipefusion_parallel_degree 4 \
+                  --use_cfg_parallel \
+                  --num_repetitions 50 \
+                  --dtype torch.float16 \
+                  --output_path results
+
+            {% endif %}
+
+            The generated video will be stored under the results directory. For the actual benchmark step runtimes, see {% if model.model == "Hunyuan Video" %}stdout.{% elif model.model in ["Wan2.1", "Wan2.2"] %}results/outputs/rank0_*.json{% elif model.model == "FLUX.1" %}results/timing.json{% elif model.model == "stable-diffusion-3.5-large"%}benchmark_results.csv{% endif %}
+
+            {% if model.model == "FLUX.1" %}You may also use ``run_usp.py`` which implements USP without modifying the default diffusers pipeline. {% endif %}
+
+      {% endfor %}
+    {% endfor %}
+
+Previous versions
+=================
+
+See
+:doc:`/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-history`
+to find documentation for previous releases of xDiT diffusion inference
+performance testing.
@@ -0,0 +1,474 @@
+:orphan:
+
+.. meta::
+   :description: Learn to validate diffusion model video generation on MI300X, MI350X and MI355X accelerators using
+                 prebuilt and optimized docker images.
+   :keywords: xDiT, diffusion, video, video generation, image, image generation, validate, benchmark
+
+************************
+xDiT diffusion inference
+************************
+
+.. caution::
+
+   This documentation does not reflect the latest version of the xDiT diffusion
+   inference performance documentation. See
+   :doc:`/how-to/rocm-for-ai/inference/xdit-diffusion-inference` for the latest
+   version.
+
+.. _xdit-video-diffusion-2513:
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.13-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   The `rocm/pytorch-xdit <{{ docker.docker_hub_url }}>`_ Docker image offers
+   a prebuilt, optimized environment based on `xDiT
+   <https://github.com/xdit-project/xDiT>`_ for benchmarking diffusion model
+   video and image generation on AMD Instinct MI355X, MI350X (gfx950), MI325X,
+   and MI300X (gfx942) GPUs.
+
+   The image runs a preview version of ROCm using the new `TheRock
+   <https://github.com/ROCm/TheRock>`__ build system and includes the following
+   components:
+
+   .. dropdown:: Software components - {{ docker.pull_tag.split('-')|last }}
+
+      .. list-table::
+         :header-rows: 1
+
+         * - Software component
+           - Version
+
+         {% for component_name, component_data in docker.components.items() %}
+         * - `{{ component_name }} <{{ component_data.url }}>`_
+           - {{ component_data.version }}
+         {% endfor %}
+
+Follow this guide to pull the required image, spin up a container, download the model, and run a benchmark.
+For preview and development releases, see `amdsiloai/pytorch-xdit <https://hub.docker.com/r/amdsiloai/pytorch-xdit>`_.
+
+What's new
+==========
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.13-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   {% for item in docker.whats_new %}
+   * {{ item }}
+   {% endfor %}
+
+.. _xdit-video-diffusion-supported-models-2513:
+
+Supported models
+================
+
+The following models are supported for inference performance benchmarking.
+Some instructions, commands, and recommendations in this documentation might
+vary by model -- select one to get started.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.13-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   .. raw:: html
+
+      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
+          <div class="row gx-0">
+              <div class="col-2 me-1 px-2 model-param-head">Model</div>
+              <div class="row col-10 pe-0">
+        {% for model_group in docker.supported_models %}
+               <div class="col-6 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.js_tag }}" tabindex="0">{{ model_group.group }}</div>
+        {% endfor %}
+              </div>
+          </div>
+
+          <div class="row gx-0 pt-1">
+              <div class="col-2 me-1 px-2 model-param-head">Variant</div>
+              <div class="row col-10 pe-0">
+        {% for model_group in docker.supported_models %}
+            {% set models = model_group.models %}
+            {% for model in models %}
+                {% if models|length % 3 == 0 %}
+                <div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.js_tag }}" data-param-group="{{ model_group.js_tag }}" tabindex="0">{{ model.model }}</div>
+                {% else %}
+                <div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.js_tag }}" data-param-group="{{ model_group.js_tag }}" tabindex="0">{{ model.model }}</div>
+                {% endif %}
+            {% endfor %}
+        {% endfor %}
+              </div>
+          </div>
+      </div>
+
+   {% for model_group in docker.supported_models %}
+       {% for model in model_group.models %}
+
+   .. container:: model-doc {{ model.js_tag }}
+
+      .. note::
+
+         To learn more about your specific model see the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`_
+         or visit the `GitHub page <{{ model.github }}>`__. Note that some models require access authorization before use via an
+         external license agreement through a third party.
+
+       {% endfor %}
+   {% endfor %}
+
+Performance measurements
+========================
+
+To evaluate performance, the `Performance results with AMD ROCm software
+<https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html#tabs-a8543b7e6d-item-9eda09e707-tab>`__
+page provides reference throughput and serving measurements for inferencing popular AI models.
+
+.. important::
+
+   The performance data presented in `Performance results with AMD ROCm
+   software
+   <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html#tabs-a8543b7e6d-item-9eda09e707-tab>`__
+   only reflects the latest version of this inference benchmarking environment.
+   The listed measurements should not be interpreted as the peak performance
+   achievable by AMD Instinct GPUs or ROCm software.
+
+System validation
+=================
+
+Before running AI workloads, it's important to validate that your AMD hardware is configured
+correctly and performing optimally.
+
+If you have already validated your system settings, including aspects like NUMA auto-balancing, you
+can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
+optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
+before starting.
+
+To test for optimal performance, consult the recommended :ref:`System health benchmarks
+<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
+system's configuration.
+
+Pull the Docker image
+=====================
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.13-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   For this tutorial, it's recommended to use the latest ``{{ docker.pull_tag }}`` Docker image.
+   Pull the image using the following command:
+
+   .. code-block:: shell
+
+      docker pull {{ docker.pull_tag }}
+
+Validate and benchmark
+======================
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.13-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   Once the image has been downloaded you can follow these steps to
+   run benchmarks and generate outputs.
+
+   {% for model_group in docker.supported_models %}
+     {% for model in model_group.models %}
+
+   .. container:: model-doc {{model.js_tag}}
+
+      The following commands are written for {{ model.model }}.
+      See :ref:`xdit-video-diffusion-supported-models-2513` to switch to another available model.
+
+     {% endfor %}
+   {% endfor %}
+
+Choose your setup method
+------------------------
+
+You can either use an existing Hugging Face cache or download the model fresh inside the container.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.13-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   {% for model_group in docker.supported_models %}
+     {% for model in model_group.models %}
+   .. container:: model-doc {{model.js_tag}}
+
+      .. tab-set::
+
+         .. tab-item:: Option 1: Use existing Hugging Face cache
+
+            If you already have models downloaded on your host system, you can mount your existing cache.
+
+            1. Set your Hugging Face cache location.
+
+               .. code-block:: shell
+
+                  export HF_HOME=/your/hf_cache/location
+
+            2. Download the model (if not already cached).
+
+               .. code-block:: shell
+
+                  huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
+
+            3. Launch the container with mounted cache.
+
+               .. code-block:: shell
+
+                  docker run \
+                      -it --rm \
+                      --cap-add=SYS_PTRACE \
+                      --security-opt seccomp=unconfined \
+                      --user root \
+                      --device=/dev/kfd \
+                      --device=/dev/dri \
+                      --group-add video \
+                      --ipc=host \
+                      --network host \
+                      --privileged \
+                      --shm-size 128G \
+                      --name pytorch-xdit \
+                      -e HSA_NO_SCRATCH_RECLAIM=1 \
+                      -e OMP_NUM_THREADS=16 \
+                      -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+                      -e HF_HOME=/app/huggingface_models \
+                      -v $HF_HOME:/app/huggingface_models \
+                      {{ docker.pull_tag }}
+
+         .. tab-item:: Option 2: Download inside container
+
+            If you prefer to keep the container self-contained or don't have an existing cache.
+
+            1. Launch the container
+
+               .. code-block:: shell
+
+                  docker run \
+                      -it --rm \
+                      --cap-add=SYS_PTRACE \
+                      --security-opt seccomp=unconfined \
+                      --user root \
+                      --device=/dev/kfd \
+                      --device=/dev/dri \
+                      --group-add video \
+                      --ipc=host \
+                      --network host \
+                      --privileged \
+                      --shm-size 128G \
+                      --name pytorch-xdit \
+                      -e HSA_NO_SCRATCH_RECLAIM=1 \
+                      -e OMP_NUM_THREADS=16 \
+                      -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+                      {{ docker.pull_tag }}
+
+            2. Inside the container, set the Hugging Face cache location and download the model.
+
+               .. code-block:: shell
+
+                  export HF_HOME=/app/huggingface_models
+                  huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
+
+               .. warning::
+
+                  Models will be downloaded to the container's filesystem and will be lost when the container is removed unless you persist the data with a volume.
+     {% endfor %}
+   {% endfor %}
+
+Run inference
+=============
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.13-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   {% for model_group in docker.supported_models %}
+     {% for model in model_group.models %}
+
+   .. container:: model-doc {{ model.js_tag }}
+
+      .. tab-set::
+
+         .. tab-item:: MAD-integrated benchmarking
+
+            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
+               directory and install the required packages on the host machine.
+
+               .. code-block:: shell
+
+                  git clone https://github.com/ROCm/MAD
+                  cd MAD
+                  pip install -r requirements.txt
+
+            2. On the host machine, use this command to run the performance benchmark test on
+               the `{{model.model}} <{{ model.url }}>`_ model using one node.
+
+               .. code-block:: shell
+
+                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
+                  madengine run \
+                      --tags {{model.mad_tag}} \
+                      --keep-model-dir \
+                      --live-output
+
+            MAD launches a Docker container with the name
+            ``container_ci-{{model.mad_tag}}``. The throughput and serving reports of the
+            model are collected in the following paths: ``{{ model.mad_tag }}_throughput.csv``
+            and ``{{ model.mad_tag }}_serving.csv``.
+
+         .. tab-item:: Standalone benchmarking
+
+            To run the benchmarks for {{ model.model }}, use the following command:
+
+            .. code-block:: shell
+            {% if model.model == "Hunyuan Video" %}
+               cd /app/Hunyuanvideo
+               mkdir results
+
+               torchrun --nproc_per_node=8 run.py \
+                  --model {{ model.model_repo }} \
+                  --prompt "In the large cage, two puppies were wagging their tails at each other." \
+                  --height 720 --width 1280 --num_frames 129 \
+                  --num_inference_steps 50 --warmup_steps 1 --n_repeats 1 \
+                  --ulysses_degree 8 \
+                  --enable_tiling --enable_slicing \
+                  --use_torch_compile \
+                  --bench_output results
+
+            {% endif %}
+            {% if model.model == "Wan2.1" %}
+               cd /app/Wan
+               mkdir results
+
+               torchrun --nproc_per_node=8 /app/Wan/run.py \
+                  --task i2v \
+                  --height 720 \
+                  --width 1280 \
+                  --model {{ model.model_repo }} \
+                  --img_file_path /app/Wan/i2v_input.JPG \
+                  --ulysses_degree 8 \
+                  --seed 42 \
+                  --num_frames 81 \
+                  --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \
+                  --num_repetitions 1 \
+                  --num_inference_steps 40 \
+                  --use_torch_compile
+
+            {% endif %}
+            {% if model.model == "Wan2.2" %}
+               cd /app/Wan
+               mkdir results
+
+               torchrun --nproc_per_node=8 /app/Wan/run.py \
+                  --task i2v \
+                  --height 720 \
+                  --width 1280 \
+                  --model {{ model.model_repo }} \
+                  --img_file_path /app/Wan/i2v_input.JPG \
+                  --ulysses_degree 8 \
+                  --seed 42 \
+                  --num_frames 81 \
+                  --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \
+                  --num_repetitions 1 \
+                  --num_inference_steps 40 \
+                  --use_torch_compile
+
+            {% endif %}
+
+            {% if model.model == "FLUX.1" %}
+               cd /app/Flux
+               mkdir results
+
+               torchrun --nproc_per_node=8 /app/Flux/run.py \
+                  --model {{ model.model_repo }} \
+                  --seed 42 \
+                  --prompt "A small cat" \
+                  --height 1024 \
+                  --width 1024 \
+                  --num_inference_steps 25 \
+                  --max_sequence_length 256 \
+                  --warmup_steps 5 \
+                  --no_use_resolution_binning \
+                  --ulysses_degree 8 \
+                  --use_torch_compile \
+                  --num_repetitions 50
+
+            {% endif %}
+
+            {% if model.model == "FLUX.1 Kontext" %}
+               cd /app/Flux
+               mkdir results
+
+               torchrun --nproc_per_node=8 /app/Flux/run_usp.py \
+                  --model {{ model.model_repo }} \
+                  --seed 42 \
+                  --prompt "Add a cool hat to the cat" \
+                  --height 1024 \
+                  --width 1024 \
+                  --num_inference_steps 30 \
+                  --max_sequence_length 512 \
+                  --warmup_steps 5 \
+                  --no_use_resolution_binning \
+                  --ulysses_degree 8 \
+                  --use_torch_compile \
+                  --img_file_path /app/Flux/cat.png \
+                  --model_type flux_kontext \
+                  --guidance_scale 2.5 \
+                  --num_repetitions 25
+
+            {% endif %}
+
+            {% if model.model == "FLUX.2" %}
+               cd /app/Flux
+               mkdir results
+
+               torchrun --nproc_per_node=8 /app/Flux/run_usp.py \
+                  --model {{ model.model_repo }} \
+                  --seed 42 \
+                  --prompt "Add a cool hat to the cat" \
+                  --height 1024 \
+                  --width 1024 \
+                  --num_inference_steps 50 \
+                  --max_sequence_length 512 \
+                  --warmup_steps 5 \
+                  --no_use_resolution_binning \
+                  --ulysses_degree 8 \
+                  --use_torch_compile \
+                  --img_file_paths /app/Flux/cat.png \
+                  --model_type flux2 \
+                  --guidance_scale 4.0 \
+                  --num_repetitions 25
+
+            {% endif %}
+
+            {% if model.model == "stable-diffusion-3.5-large" %}
+               cd /app/StableDiffusion3.5
+               mkdir results
+
+               torchrun --nproc_per_node=8 /app/StableDiffusion3.5/run.py \
+                  --model {{ model.model_repo }} \
+                  --num_inference_steps 28 \
+                  --prompt "A capybara holding a sign that reads Hello World" \
+                  --use_torch_compile \
+                  --pipefusion_parallel_degree 4 \
+                  --use_cfg_parallel \
+                  --num_repetitions 50 \
+                  --dtype torch.float16 \
+                  --output_path results
+
+            {% endif %}
+
+            The generated video will be stored under the results directory. For the actual benchmark step runtimes, see {% if model.model == "Hunyuan Video" %}stdout.{% elif model.model in ["Wan2.1", "Wan2.2"] %}results/outputs/rank0_*.json{% elif model.model in ["FLUX.1", "FLUX.1 Kontext", "FLUX.2"] %}results/timing.json{% elif model.model == "stable-diffusion-3.5-large"%}benchmark_results.csv{% endif %}
+
+            {% if model.model == "FLUX.1" %}You may also use ``run_usp.py`` which implements USP without modifying the default diffusers pipeline. {% endif %}
+
+      {% endfor %}
+    {% endfor %}
+
+Previous versions
+=================
+
+See
+:doc:`/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-history`
+to find documentation for previous releases of xDiT diffusion inference
+performance testing.
@@ -0,0 +1,322 @@
+:orphan:
+
+.. meta::
+   :description: Learn to validate diffusion model video generation on MI300X, MI350X and MI355X accelerators using
+                 prebuilt and optimized docker images.
+   :keywords: xDiT, diffusion, video, video generation, image, image generation, validate, benchmark
+
+************************
+xDiT diffusion inference
+************************
+
+.. caution::
+
+   This documentation does not reflect the latest version of the xDiT diffusion
+   inference performance documentation. See
+   :doc:`/how-to/rocm-for-ai/inference/xdit-diffusion-inference` for the latest
+   version.
+
+.. _xdit-video-diffusion-v261-v261:
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.1-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   The `rocm/pytorch-xdit <{{ docker.docker_hub_url }}>`_ Docker image offers a prebuilt, optimized environment based on `xDiT <https://github.com/xdit-project/xDiT>`_ for
+   benchmarking diffusion model video and image generation on gfx942 and gfx950 series (AMD Instinct™ MI300X, MI325X, MI350X, and MI355X) GPUs.
+   The image runs ROCm **{{docker.ROCm}}** (preview) based on `TheRock <https://github.com/ROCm/TheRock>`_
+   and includes the following components:
+
+   .. dropdown:: Software components - {{ docker.pull_tag.split('-')|last }}
+
+      .. list-table::
+         :header-rows: 1
+
+         * - Software component
+           - Version
+
+         {% for component_name, component_data in docker.components.items() %}
+         * - `{{ component_name }} <{{ component_data.url }}>`_
+           - {{ component_data.version }}
+         {% endfor %}
+
+Follow this guide to pull the required image, spin up a container, download the model, and run a benchmark.
+For preview and development releases, see `amdsiloai/pytorch-xdit <https://hub.docker.com/r/amdsiloai/pytorch-xdit>`_.
+
+What's new
+==========
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.1-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   {% for item in docker.whats_new %}
+   * {{ item }}
+   {% endfor %}
+
+.. _xdit-video-diffusion-supported-models-v261:
+
+Supported models
+================
+
+The following models are supported for inference performance benchmarking.
+Some instructions, commands, and recommendations in this documentation might
+vary by model -- select one to get started.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.1-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   .. raw:: html
+
+      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
+          <div class="row gx-0">
+              <div class="col-2 me-1 px-2 model-param-head">Model</div>
+              <div class="row col-10 pe-0">
+        {% for model_group in docker.supported_models %}
+               <div class="col-6 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.js_tag }}" tabindex="0">{{ model_group.group }}</div>
+        {% endfor %}
+              </div>
+          </div>
+
+          <div class="row gx-0 pt-1">
+              <div class="col-2 me-1 px-2 model-param-head">Variant</div>
+              <div class="row col-10 pe-0">
+        {% for model_group in docker.supported_models %}
+            {% set models = model_group.models %}
+            {% for model in models %}
+                {% if models|length % 3 == 0 %}
+                <div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.js_tag }}" data-param-group="{{ model_group.js_tag }}" tabindex="0">{{ model.model }}</div>
+                {% else %}
+                <div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.js_tag }}" data-param-group="{{ model_group.js_tag }}" tabindex="0">{{ model.model }}</div>
+                {% endif %}
+            {% endfor %}
+        {% endfor %}
+              </div>
+          </div>
+      </div>
+
+   {% for model_group in docker.supported_models %}
+       {% for model in model_group.models %}
+
+   .. container:: model-doc {{ model.js_tag }}
+
+      .. note::
+
+         To learn more about your specific model see the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`_
+         or visit the `GitHub page <{{ model.github }}>`__. Note that some models require access authorization before use via an
+         external license agreement through a third party.
+
+       {% endfor %}
+   {% endfor %}
+
+System validation
+=================
+
+Before running AI workloads, it's important to validate that your AMD hardware is configured
+correctly and performing optimally.
+
+If you have already validated your system settings, including aspects like NUMA auto-balancing, you
+can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
+optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
+before starting.
+
+To test for optimal performance, consult the recommended :ref:`System health benchmarks
+<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
+system's configuration.
+
+Pull the Docker image
+=====================
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.1-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   For this tutorial, it's recommended to use the latest ``{{ docker.pull_tag }}`` Docker image.
+   Pull the image using the following command:
+
+   .. code-block:: shell
+
+      docker pull {{ docker.pull_tag }}
+
+Validate and benchmark
+======================
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.1-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   Once the image has been downloaded you can follow these steps to
+   run benchmarks and generate outputs.
+
+   {% for model_group in docker.supported_models %}
+     {% for model in model_group.models %}
+
+   .. container:: model-doc {{model.js_tag}}
+
+      The following commands are written for {{ model.model }}.
+      See :ref:`xdit-video-diffusion-supported-models-v261` to switch to another available model.
+
+     {% endfor %}
+   {% endfor %}
+
+Choose your setup method
+------------------------
+
+You can either use an existing Hugging Face cache or download the model fresh inside the container.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.1-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   {% for model_group in docker.supported_models %}
+     {% for model in model_group.models %}
+   .. container:: model-doc {{model.js_tag}}
+
+      .. tab-set::
+
+         .. tab-item:: Option 1: Use existing Hugging Face cache
+
+            If you already have models downloaded on your host system, you can mount your existing cache.
+
+            1. Set your Hugging Face cache location.
+
+               .. code-block:: shell
+
+                  export HF_HOME=/your/hf_cache/location
+
+            2. Download the model (if not already cached).
+
+               .. code-block:: shell
+
+                  huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
+
+            3. Launch the container with mounted cache.
+
+               .. code-block:: shell
+
+                  docker run \
+                      -it --rm \
+                      --cap-add=SYS_PTRACE \
+                      --security-opt seccomp=unconfined \
+                      --user root \
+                      --device=/dev/kfd \
+                      --device=/dev/dri \
+                      --group-add video \
+                      --ipc=host \
+                      --network host \
+                      --privileged \
+                      --shm-size 128G \
+                      --name pytorch-xdit \
+                      -e HSA_NO_SCRATCH_RECLAIM=1 \
+                      -e OMP_NUM_THREADS=16 \
+                      -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+                      -e HF_HOME=/app/huggingface_models \
+                      -v $HF_HOME:/app/huggingface_models \
+                      {{ docker.pull_tag }}
+
+         .. tab-item:: Option 2: Download inside container
+
+            If you prefer to keep the container self-contained or don't have an existing cache.
+
+            1. Launch the container
+
+               .. code-block:: shell
+
+                  docker run \
+                      -it --rm \
+                      --cap-add=SYS_PTRACE \
+                      --security-opt seccomp=unconfined \
+                      --user root \
+                      --device=/dev/kfd \
+                      --device=/dev/dri \
+                      --group-add video \
+                      --ipc=host \
+                      --network host \
+                      --privileged \
+                      --shm-size 128G \
+                      --name pytorch-xdit \
+                      -e HSA_NO_SCRATCH_RECLAIM=1 \
+                      -e OMP_NUM_THREADS=16 \
+                      -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+                      {{ docker.pull_tag }}
+
+            2. Inside the container, set the Hugging Face cache location and download the model.
+
+               .. code-block:: shell
+
+                  export HF_HOME=/app/huggingface_models
+                  huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
+
+               .. warning::
+
+                  Models will be downloaded to the container's filesystem and will be lost when the container is removed unless you persist the data with a volume.
+     {% endfor %}
+   {% endfor %}
+
+Run inference
+=============
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.1-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   {% for model_group in docker.supported_models %}
+     {% for model in model_group.models %}
+
+   .. container:: model-doc {{ model.js_tag }}
+
+      .. tab-set::
+
+         .. tab-item:: MAD-integrated benchmarking
+
+            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
+               directory and install the required packages on the host machine.
+
+               .. code-block:: shell
+
+                  git clone https://github.com/ROCm/MAD
+                  cd MAD
+                  pip install -r requirements.txt
+
+            2. On the host machine, use this command to run the performance benchmark test on
+               the `{{model.model}} <{{ model.url }}>`_ model using one node.
+
+               .. code-block:: shell
+
+                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
+                  madengine run \
+                      --tags {{model.mad_tag}} \
+                      --keep-model-dir \
+                      --live-output
+
+            MAD launches a Docker container with the name
+            ``container_ci-{{model.mad_tag}}``. The throughput and serving reports of the
+            model are collected in the following paths: ``{{ model.mad_tag }}_throughput.csv``
+            and ``{{ model.mad_tag }}_serving.csv``.
+
+         .. tab-item:: Standalone benchmarking
+
+            To run the benchmarks for {{ model.model }}, use the following command:
+
+            .. code-block:: shell
+
+               {{ model.benchmark_command
+                  | map('replace', '{model_repo}', model.model_repo)
+                  | map('trim')
+                  | join('\n               ') }}
+
+            The generated video will be stored under the results directory.
+
+            {% if model.model == "FLUX.1" %}You may also use ``run_usp.py`` which implements USP without modifying the default diffusers pipeline. {% endif %}
+
+      {% endfor %}
+    {% endfor %}
+
+Previous versions
+=================
+
+See
+:doc:`/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-history`
+to find documentation for previous releases of xDiT diffusion inference
+performance testing.
@@ -0,0 +1,320 @@
+:orphan:
+
+.. meta::
+   :description: Learn to validate diffusion model video generation on MI300X, MI350X and MI355X accelerators using
+                 prebuilt and optimized docker images.
+   :keywords: xDiT, diffusion, video, video generation, image, image generation, validate, benchmark
+
+************************
+xDiT diffusion inference
+************************
+
+.. caution::
+
+   This documentation does not reflect the latest version of the xDiT diffusion
+   inference performance documentation. See
+   :doc:`/how-to/rocm-for-ai/inference/xdit-diffusion-inference` for the latest
+   version.
+
+.. _xdit-video-diffusion-262:
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.2-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   The `rocm/pytorch-xdit <{{ docker.docker_hub_url }}>`_ Docker image offers a prebuilt, optimized environment based on `xDiT <https://github.com/xdit-project/xDiT>`_ for
+   benchmarking diffusion model video and image generation on gfx942 and gfx950 series (AMD Instinct™ MI300X, MI325X, MI350X, and MI355X) GPUs.
+   The image runs ROCm **{{docker.ROCm}}** (preview) based on `TheRock <https://github.com/ROCm/TheRock>`_
+   and includes the following components:
+
+   .. dropdown:: Software components - {{ docker.pull_tag.split('-')|last }}
+
+      .. list-table::
+         :header-rows: 1
+
+         * - Software component
+           - Version
+
+         {% for component_name, component_data in docker.components.items() %}
+         * - `{{ component_name }} <{{ component_data.url }}>`_
+           - {{ component_data.version }}
+         {% endfor %}
+
+Follow this guide to pull the required image, spin up a container, download the model, and run a benchmark.
+For preview and development releases, see `amdsiloai/pytorch-xdit <https://hub.docker.com/r/amdsiloai/pytorch-xdit>`_.
+
+What's new
+==========
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.2-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   {% for item in docker.whats_new %}
+   * {{ item }}
+   {% endfor %}
+
+.. _xdit-video-diffusion-262-supported-models:
+
+Supported models
+================
+
+The following models are supported for inference performance benchmarking.
+Some instructions, commands, and recommendations in this documentation might
+vary by model -- select one to get started.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.2-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   .. raw:: html
+
+      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
+          <div class="row gx-0">
+              <div class="col-2 me-1 px-2 model-param-head">Model</div>
+              <div class="row col-10 pe-0">
+        {% for model_group in docker.supported_models %}
+               <div class="col-6 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.js_tag }}" tabindex="0">{{ model_group.group }}</div>
+        {% endfor %}
+              </div>
+          </div>
+
+          <div class="row gx-0 pt-1">
+              <div class="col-2 me-1 px-2 model-param-head">Variant</div>
+              <div class="row col-10 pe-0">
+        {% for model_group in docker.supported_models %}
+            {% set models = model_group.models %}
+            {% for model in models %}
+                {% if models|length % 3 == 0 %}
+                <div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.js_tag }}" data-param-group="{{ model_group.js_tag }}" tabindex="0">{{ model.model }}</div>
+                {% else %}
+                <div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.js_tag }}" data-param-group="{{ model_group.js_tag }}" tabindex="0">{{ model.model }}</div>
+                {% endif %}
+            {% endfor %}
+        {% endfor %}
+              </div>
+          </div>
+      </div>
+
+   {% for model_group in docker.supported_models %}
+       {% for model in model_group.models %}
+
+   .. container:: model-doc {{ model.js_tag }}
+
+      .. note::
+
+         To learn more about your specific model see the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`_
+         or visit the `GitHub page <{{ model.github }}>`__. Note that some models require access authorization before use via an
+         external license agreement through a third party.
+
+       {% endfor %}
+   {% endfor %}
+
+System validation
+=================
+
+Before running AI workloads, it's important to validate that your AMD hardware is configured
+correctly and performing optimally.
+
+If you have already validated your system settings, including aspects like NUMA auto-balancing, you
+can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
+optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
+before starting.
+
+To test for optimal performance, consult the recommended :ref:`System health benchmarks
+<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
+system's configuration.
+
+Pull the Docker image
+=====================
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.2-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   For this tutorial, it's recommended to use the latest ``{{ docker.pull_tag }}`` Docker image.
+   Pull the image using the following command:
+
+   .. code-block:: shell
+
+      docker pull {{ docker.pull_tag }}
+
+Validate and benchmark
+======================
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.2-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   Once the image has been downloaded you can follow these steps to
+   run benchmarks and generate outputs.
+
+   {% for model_group in docker.supported_models %}
+     {% for model in model_group.models %}
+
+   .. container:: model-doc {{model.js_tag}}
+
+      The following commands are written for {{ model.model }}.
+      See :ref:`xdit-video-diffusion-262-supported-models` to switch to another available model.
+
+     {% endfor %}
+   {% endfor %}
+
+Choose your setup method
+------------------------
+
+You can either use an existing Hugging Face cache or download the model fresh inside the container.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.2-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   {% for model_group in docker.supported_models %}
+     {% for model in model_group.models %}
+   .. container:: model-doc {{model.js_tag}}
+
+      .. tab-set::
+
+         .. tab-item:: Option 1: Use existing Hugging Face cache
+
+            If you already have models downloaded on your host system, you can mount your existing cache.
+
+            1. Set your Hugging Face cache location.
+
+               .. code-block:: shell
+
+                  export HF_HOME=/your/hf_cache/location
+
+            2. Download the model (if not already cached).
+
+               .. code-block:: shell
+
+                  huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
+
+            3. Launch the container with mounted cache.
+
+               .. code-block:: shell
+
+                  docker run \
+                      -it --rm \
+                      --cap-add=SYS_PTRACE \
+                      --security-opt seccomp=unconfined \
+                      --user root \
+                      --device=/dev/kfd \
+                      --device=/dev/dri \
+                      --group-add video \
+                      --ipc=host \
+                      --network host \
+                      --privileged \
+                      --shm-size 128G \
+                      --name pytorch-xdit \
+                      -e HSA_NO_SCRATCH_RECLAIM=1 \
+                      -e OMP_NUM_THREADS=16 \
+                      -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+                      -e HF_HOME=/app/huggingface_models \
+                      -v $HF_HOME:/app/huggingface_models \
+                      {{ docker.pull_tag }}
+
+         .. tab-item:: Option 2: Download inside container
+
+            If you prefer to keep the container self-contained or don't have an existing cache.
+
+            1. Launch the container
+
+               .. code-block:: shell
+
+                  docker run \
+                      -it --rm \
+                      --cap-add=SYS_PTRACE \
+                      --security-opt seccomp=unconfined \
+                      --user root \
+                      --device=/dev/kfd \
+                      --device=/dev/dri \
+                      --group-add video \
+                      --ipc=host \
+                      --network host \
+                      --privileged \
+                      --shm-size 128G \
+                      --name pytorch-xdit \
+                      -e HSA_NO_SCRATCH_RECLAIM=1 \
+                      -e OMP_NUM_THREADS=16 \
+                      -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+                      {{ docker.pull_tag }}
+
+            2. Inside the container, set the Hugging Face cache location and download the model.
+
+               .. code-block:: shell
+
+                  export HF_HOME=/app/huggingface_models
+                  huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
+
+               .. warning::
+
+                  Models will be downloaded to the container's filesystem and will be lost when the container is removed unless you persist the data with a volume.
+     {% endfor %}
+   {% endfor %}
+
+Run inference
+=============
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.2-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   {% for model_group in docker.supported_models %}
+     {% for model in model_group.models %}
+
+   .. container:: model-doc {{ model.js_tag }}
+
+      .. tab-set::
+
+         .. tab-item:: MAD-integrated benchmarking
+
+            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
+               directory and install the required packages on the host machine.
+
+               .. code-block:: shell
+
+                  git clone https://github.com/ROCm/MAD
+                  cd MAD
+                  pip install -r requirements.txt
+
+            2. On the host machine, use this command to run the performance benchmark test on
+               the `{{model.model}} <{{ model.url }}>`_ model using one node.
+
+               .. code-block:: shell
+
+                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
+                  madengine run \
+                      --tags {{model.mad_tag}} \
+                      --keep-model-dir \
+                      --live-output
+
+            MAD launches a Docker container with the name
+            ``container_ci-{{model.mad_tag}}``. The throughput and serving reports of the
+            model are collected in the following paths: ``{{ model.mad_tag }}_throughput.csv``
+            and ``{{ model.mad_tag }}_serving.csv``.
+
+         .. tab-item:: Standalone benchmarking
+
+            To run the benchmarks for {{ model.model }}, use the following command:
+
+            .. code-block:: shell
+
+               {{ model.benchmark_command
+                  | map('replace', '{model_repo}', model.model_repo)
+                  | map('trim')
+                  | join('\n               ') }}
+
+            The generated content and timing information will be stored under the results directory.
+
+      {% endfor %}
+    {% endfor %}
+
+Previous versions
+=================
+
+See
+:doc:`/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-history`
+to find documentation for previous releases of xDiT diffusion inference
+performance testing.
@@ -0,0 +1,71 @@
+:orphan:
+
+************************************************************
+xDiT diffusion inference performance testing version history
+************************************************************
+
+This table lists previous versions of the ROCm xDiT diffusion inference performance
+testing environment. For detailed information about available models for
+benchmarking, see the version-specific documentation.
+
+.. list-table::
+   :header-rows: 1
+
+   * - Docker image tag
+     - Components
+     - Resources
+
+   * - ``rocm/pytorch-xdit:v26.3`` (latest)
+     -
+       * TheRock e40a6da
+     -
+       * :doc:`Documentation </how-to/rocm-for-ai/inference/xdit-diffusion-inference>`
+       * `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-xdit/v26.3/images/sha256-ac78a03d2911bf1b49c001d3be2e8bd745c1bc455cb49ae972825a7986880902>`__
+
+   * - ``rocm/pytorch-xdit:v26.2``
+     -
+       * `ROCm 7.11.0 preview <https://rocm.docs.amd.com/en/7.11.0-preview/about/release-notes.html>`__
+       * TheRock 1728a81
+     -
+       * :doc:`Documentation <xdit-26.2>`
+       * `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-xdit/v26.2/images/sha256-e2c504af438bb9cf60e3869c499baa5102b3d3f62141b99c49743e755ae44008>`__
+
+   * - ``rocm/pytorch-xdit:v26.1``
+     -
+       * `ROCm 7.11.0 preview <https://rocm.docs.amd.com/en/7.11.0-preview/about/release-notes.html>`__
+       * TheRock 1728a81
+     -
+       * :doc:`Documentation <xdit-26.1>`
+       * `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-xdit/v26.1/images/sha256-4e35ebcad47042a41389b992ecb3489b3b0a922e4c34c7a0dd1098733a3db513>`__
+
+   * - ``rocm/pytorch-xdit:v25.13``
+     -
+       * `ROCm 7.11.0 preview <https://rocm.docs.amd.com/en/7.11.0-preview/about/release-notes.html>`__
+       * TheRock 1728a81
+     -
+       * :doc:`Documentation <xdit-25.13>`
+       * `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-xdit/v25.13/images/sha256-81954713070d67bde08595e03f62110c8a3dd66a9ae17a77d611e01f83f0f4ef>`__
+
+   * - ``rocm/pytorch-xdit:v25.12``
+     -
+       * `ROCm 7.10.0 preview <https://rocm.docs.amd.com/en/7.10.0-preview/about/release-notes.html>`__
+       * TheRock 3e3f834
+     -
+       * :doc:`Documentation <xdit-25.12>`
+       * `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-xdit/v25.12/images/sha256-e06895132316bf3c393366b70a91eaab6755902dad0100e6e2b38310547d9256>`__
+
+   * - ``rocm/pytorch-xdit:v25.11``
+     -
+       * `ROCm 7.10.0 preview <https://rocm.docs.amd.com/en/7.10.0-preview/about/release-notes.html>`__
+       * TheRock 3e3f834
+     -
+       * :doc:`Documentation <xdit-25.11>`
+       * `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-xdit/v25.11/images/sha256-c9fa659439bb024f854b4d5eea598347251b02c341c55f66c98110832bde4216>`__
+
+   * - ``rocm/pytorch-xdit:v25.10``
+     -
+       * `ROCm 7.9.0 preview <https://rocm.docs.amd.com/en/7.9.0-preview/about/release-notes.html>`__
+       * TheRock 7afbe45
+     -
+       * :doc:`Documentation <xdit-25.10>`
+       * `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-xdit/v25.10/images/sha256-d79715ff18a9470e3f907cec8a9654d6b783c63370b091446acffc0de4d7070e>`__
@@ -0,0 +1,904 @@
+# SGLang distributed inference with MoRI
+
+This document provides a comprehensive guide for deploying a high-performance
+SGLang distributed inference serving environment on an AMD Instinct MI355X GPU
+cluster, utilizing the [MoRI (Modular RDMA
+Interface)](https://github.com/rocm/mori) communication backend for optimized
+inter-node collective operations. It also includes systematic instructions for
+benchmarking 1P2D (1 prefill 2 decode, 3 nodes) configurations using automated
+scripts.
+
+## Prerequisites
+
+The following configuration is required to implement this setup:
+
+* **Nodes:** A minimum of three GPU nodes (Virtual machines or Physical
+    machines) for wide expert parallelism (EP) evaluation.
+* **GPUs** 8x AMD Instinct MI355X GPU cards per node.
+* **Networking:**   8x AMD Pensando™ Pollara 400 AI NICs per node, providing
+  a dedicated 1:1 mapping between GPUs and network interfaces for optimal
+  inter-node communication.
+* **Orchestration:** A Slurm cluster with at least three nodes -- one for
+  prefill service and two for decode services (EP16)
+
+## System configuration
+
+This section outlines the infrastructure setup required to support your AMD
+Instinct MI355X cluster. It covers essential procedures for verifying software
+baselines and firmware versions, configuring the AMD Pensando Pollara 400 AI
+NICs for high-bandwidth networking, and applying thermal and Quality of Service
+(QoS) tunings to ensure a stable, lossless RDMA fabric.
+
+(sglang-mori-verify-baseline)=
+
+### Verify baseline software
+
+The following table outlines the validated software stack. Use the provided
+shell commands to verify the environment on each node before proceeding.
+
+| Component | Version | Verification command |
+| :--- | :--- | :--- |
+| **OS** | Ubuntu 22.04.5 LTS | `cat /etc/os-release` |
+| **Kernel** | 5.15.0-163-generic | `uname -r` |
+| **ROCm** | 7.1.1 | `amd-smi version` |
+| **PLDM bundle (firmware)** | 01.25.16.03 | [Verify BKC](#verify-best-known-configuration-bkc) |
+| **AI NIC Firmware** | 1.117.5.a.45 | `dkms status` |
+| **AI NIC Driver** | 25.11.1.001 | `dkms status` |
+
+### Verify best known configuration (BKC)
+
+The BKC defines a validated configuration of GPU firmware, baseboard firmware,
+ROCm user space components, the AMD GPU Driver, and virtualization tooling.
+These components are tested together to attain best performance and compatibility.
+
+While AMD publishes the AMD GPU driver and ROCm user space components, your
+server OEM or infrastructure provider distributes the firmware packages. AMD
+supplies those firmware images (PLDM bundles), which the OEM integrates and
+distributes.
+
+To verify the active BKC and IFWI (Integrated Firmware Image) versions via the
+Redfish API:
+
+1. Prepare credentials: Identify your BMC IP, username, and password.
+2. Run Redfish queries: Use the following commands to check the active
+   firmware inventory.
+
+     ``` bash
+     # Define BMC connection variables
+     BMC_IP="<BMC_IP>"
+     AUTH="<username>:<password>"
+
+     # Query active BKC bundle version
+     curl -X GET "https://${BMC_IP}/redfish/v1/UpdateService/FirmwareInventory/bundle_active" \
+          -u "${AUTH}" -k | json_pp
+
+     # Query active IFWI (Integrated Firmware Image)
+     curl -X GET "https://${BMC_IP}/redfish/v1/UpdateService/FirmwareInventory/firmware_active" \
+          -u "${AUTH}" -k | json_pp
+     ```
+
+### Run basic system health checks
+
+Before proceeding with software deployment, verify that all cluster nodes
+comply with the [MI355X Basic Health
+Checks](https://instinct.docs.amd.com/projects/system-acceptance/en/latest/gpus/mi355x.html#basic-health-checks)
+Key requirements include specific kernel boot arguments, minimum system memory
+thresholds, PCIe Gen5 link stability, and so on.
+
+### Install AMD Pensando Pollara 400 AI NIC drivers
+
+For detailed instructions on upgrading the firmware and installing drivers for
+the AMD Pensando Pollara 400 AI NIC, refer to the [AMD Instinct System
+Acceptance
+Guide](https://instinct.docs.amd.com/projects/system-acceptance/en/latest/network/nic-installation.html#amd-pensando-pollara-400-ai-nic).
+After installation, verify the active firmware version on all NICs to ensure it
+matches the software baseline. See [Verify baseline software](#verify-best-known-configuration-bkc).
+
+To display the current firmware version for all AI NICs, use the following command.
+
+```bash
+sudo nicctl show version firmware
+```
+
+### Configure thermal management (fan speed)
+
+For systems equipped with 400G optics, standard fan profiles are often
+insufficient for maintaining stable operating temperatures. To prevent thermal
+throttling or optics failure, the system fans must be set to `FullSpeed`.
+
+* Requirement: A fan speed of approximately 25,000 RPM is required to maintain
+  the AI NIC modules at an optimal operating temperature (~50°C).
+
+* Constraint: Default profiles (typically around 4,000 RPM) and "Performance IO"
+  settings (around 9,000 RPM) do not provide adequate airflow for 400G optical
+  transceivers.
+
+#### Configure fan speed via Redfish (Supermicro)
+
+Run the following command to set the fan mode to `FullSpeed` through the BMC:
+
+``` bash
+# Define BMC connection variables
+BMC_IP="<BMC_IP>"
+AUTH="<username>:<password>"
+
+# Set Fan Mode to FullSpeed
+curl -X PATCH "https://${BMC_IP}/redfish/v1/Managers/1/Oem/Supermicro/FanMode" \
+     -k -u "${AUTH}" \
+     -H "Content-Type: application/json" \
+     -d '{"Mode": "FullSpeed"}'
+```
+
+### Configure your backend network (netplan)
+
+Configure the backend NICs for high-bandwidth inter-node communication. Suppose
+the GPU’s eight network interface controllers (NICs) are `benic1p1` to
+`benic8p1`. Each NIC must have its own subnet that is disjoint from the others.
+Each node needs a unique IP address on each subnet. You should use the same
+final octet in each subnet for a given node. For example, one node would have
+the addresses `192.168.1.36`, `192.168.2.36`, and so on. Another node would
+have `192.168.1.37`, `192.168.2.37`, and so on. Ensure MTU is set to `9000`.
+
+```{note}
+Ensure you identify the correct interface names for your system using ip link
+before applying this configuration.
+```
+
+For example, your `/etc/netplan/70-backend.yaml` should look like the
+following:
+
+```yaml
+network:
+  ethernets:
+    benic8p1:
+      addresses:
+      - 192.168.8.38/31
+      match:
+        macaddress: 04:90:81:2a:34:08
+      mtu: 9000
+      routes:
+      - table: 108
+        to: 0.0.0.0/0
+        via: 192.168.8.39
+      routing-policy:
+      - from: 192.168.8.38
+        table: 108
+      set-name: benic8p1
+    benic7p1:
+      addresses:
+      - 192.168.7.38/31
+      match:
+        macaddress: 04:90:81:2b:82:40
+      mtu: 9000
+      routes:
+      - table: 107
+        to: 0.0.0.0/0
+        via: 192.168.7.39
+      routing-policy:
+      - from: 192.168.7.38
+        table: 107
+      set-name: benic7p1
+    benic6p1:
+      addresses:
+      - 192.168.6.38/31
+      match:
+        macaddress: 04:90:81:30:c9:30
+      mtu: 9000
+      routes:
+      - table: 106
+        to: 0.0.0.0/0
+        via: 192.168.6.39
+      routing-policy:
+      - from: 192.168.6.38
+        table: 106
+      set-name: benic6p1
+    benic5p1:
+      addresses:
+      - 192.168.5.38/31
+      match:
+        macaddress: 04:90:81:2a:23:40
+      mtu: 9000
+      routes:
+      - table: 105
+        to: 0.0.0.0/0
+        via: 192.168.5.39
+      routing-policy:
+      - from: 192.168.5.38
+        table: 105
+      set-name: benic5p1
+    benic4p1:
+      addresses:
+      - 192.168.4.38/31
+      match:
+        macaddress: 04:90:81:2d:69:60
+      mtu: 9000
+      routes:
+      - table: 104
+        to: 0.0.0.0/0
+        via: 192.168.4.39
+      routing-policy:
+      - from: 192.168.4.38
+        table: 104
+      set-name: benic4p1
+    benic3p1:
+      addresses:
+      - 192.168.3.38/31
+      match:
+        macaddress: 04:90:81:2a:2c:40
+      mtu: 9000
+      routes:
+      - table: 103
+        to: 0.0.0.0/0
+        via: 192.168.3.39
+      routing-policy:
+      - from: 192.168.3.38
+        table: 103
+      set-name: benic3p1
+    benic2p1:
+      addresses:
+      - 192.168.2.38/31
+      match:
+        macaddress: 04:90:81:30:d5:30
+      mtu: 9000
+      routes:
+      - table: 102
+        to: 0.0.0.0/0
+        via: 192.168.2.39
+      routing-policy:
+      - from: 192.168.2.38
+        table: 102
+      set-name: benic2p1
+    benic1p1:
+      addresses:
+      - 192.168.1.38/31
+      match:
+        macaddress: 04:90:81:30:e4:00
+      mtu: 9000
+      routes:
+      - table: 101
+        to: 0.0.0.0/0
+        via: 192.168.1.39
+      routing-policy:
+      - from: 192.168.1.38
+        table: 101
+      set-name: benic1p1
+```
+
+To apply the configuration, use the following command.
+
+```bash
+sudo netplan apply
+```
+
+To verify your configuration, use the following command.
+
+```bash
+sudo apt install -y net-tools && ip -br a
+```
+
+### Configure Quality of Service (QoS) and Congestion Control (DCQCN)
+
+To ensure lossless communication and optimal performance for RDMA traffic, the
+network must be configured with specific QoS and Data Center Quantized
+Congestion Notification (DCQCN) settings.
+
+The following configuration achieves:
+• It enables RX and TX Pause frames on the ports
+• Maps DSCP 24 (Data) to Q3 and DSCP 46 (CNP) to Q6, all other DSCP to Q0
+• Enables PFC for Q3
+• Scheduling : 99% to Q3, 1% to Q0 and strict priority for Q6
+
+#### Configure DCQCN
+
+Create and run a `/nfsdata/enable_dcqcn.sh` script to initialize congestion
+control parameters.
+
+``` bash
+# !/bin/bash
+
+TOKEN_BUCKET_SIZE=800000
+AI_RATE=160
+ALPHA_UPDATE_INTERVAL=1
+ALPHA_UPDATE_G=512
+INITIAL_ALPHA_VALUE=64
+RATE_INCREASE_BYTE_COUNT=431068
+HAI_RATE=300
+RATE_REDUCE_MONITOR_PERIOD=1
+RATE_INCREASE_THRESHOLD=1
+RATE_INCREASE_INTERVAL=1
+CNP_DSCP=46
+
+ROCE_DEVICES=$(ibv_devices | grep ionic_ | awk '{print $1}' | paste -sd " ")
+for roce_dev in $ROCE_DEVICES
+do
+    sudo nicctl update dcqcn -r $roce_dev -i 1 \
+    --token-bucket-size $TOKEN_BUCKET_SIZE \
+    --ai-rate $AI_RATE \
+    --alpha-update-interval $ALPHA_UPDATE_INTERVAL \
+    --alpha-update-g $ALPHA_UPDATE_G \
+    --initial-alpha-value $INITIAL_ALPHA_VALUE \
+    --rate-increase-byte-count $RATE_INCREASE_BYTE_COUNT \
+    --hai-rate $HAI_RATE \
+    --rate-reduce-monitor-period $RATE_REDUCE_MONITOR_PERIOD \
+    --rate-increase-threshold $RATE_INCREASE_THRESHOLD  \
+    --rate-increase-interval $RATE_INCREASE_INTERVAL \
+    --cnp-dscp $CNP_DSCP
+done
+```
+
+#### Configure QoS and PFC
+
+Create and run `/nfsdata/qos.sh` to set up traffic classes and scheduling.
+
+``` bash
+#!/bin/bash
+# qos.sh
+
+# Enable PFC and Auto-negotiation on all ports
+for i in $(sudo nicctl show port | grep Port | awk {'print $3'}); do sudo nicctl update port -p $i --pause-type pfc --rx-pause enable --tx-pause enable; done
+for i in $(sudo nicctl show port | grep Port | awk '{print $3}'); do sudo nicctl update port --port $i --auto-neg enable; done
+
+# Define Priorities
+cts_dscp=46
+cts_prio=6
+data_dscp=24
+data_prio=3
+default_prio=0
+cnp_dscp=46
+cnp_prio=6
+
+sudo nicctl update qos pfc --priority 0 --no-drop disable
+sudo nicctl update qos dscp-to-purpose --dscp 48 --purpose none
+sudo nicctl update qos dscp-to-purpose --dscp 46 --purpose none
+sudo nicctl update qos --classification-type pcp
+sudo nicctl update qos --classification-type dscp
+sudo nicctl update qos dscp-to-priority --dscp 0-63 --priority 0
+sudo nicctl update qos dscp-to-priority --dscp 0-23,25-45,47-63 --priority $default_prio
+sudo nicctl update qos dscp-to-priority --dscp $cts_dscp --priority $cts_prio
+sudo nicctl update qos dscp-to-priority --dscp $data_dscp --priority $data_prio
+sudo nicctl update qos dscp-to-priority --dscp $cnp_dscp --priority $cnp_prio
+sudo nicctl update qos pfc --priority $data_prio --no-drop enable
+sudo nicctl update qos scheduling --priority $data_prio,$default_prio,$cts_prio --dwrr 99,1,0 --rate-limit 0,0,10
+```
+
+#### Verification your configuration
+
+Verify the configuration using `nicctl`.
+
+* Verify QoS classification:
+
+  ``` bash
+  sudo nicctl show qos
+  ```
+
+  Expected QoS output:
+
+  ``` bash
+  NIC  : 42424650-4c32-3531-3230-303443000000 (0000:f6:00.0)
+
+  Port : 04908130-a7a0-4242-4242-000011010000
+
+  Classification type         : DSCP
+
+  DSCP-to-priority :
+  DSCP bitmap               : 0xffffbffffeffffff ==> priority : 0
+  DSCP bitmap               : 0x0000000001000000 ==> priority : 3
+  DSCP bitmap               : 0x0000400000000000 ==> priority : 6
+  DSCP                      : 0-23, 25-45, 47-63 ==> priority : 0
+  DSCP                      : 24 ==> priority : 3
+  DSCP                      : 46 ==> priority : 6
+  ```
+
+* Verify DCQCN and scheduling:
+
+  ``` bash
+  sudo nicctl show dcqcn
+  ```
+
+  Expected DCQCN and scheduling output:
+
+  ``` bash
+  NIC : 42424650-4c32-3531-3230-303443000000 (0000:f6:00.0)
+  ------------------------------------------------------------------------------------------
+
+  Lif id                                     : 43000070-0100-0000-4242-04908130a7a0
+  ROCE device                                : ionic_7
+  DCQCN profile id                         : 1
+  Status                                   : Enabled
+  Rate increase in AI phase                : 160
+  Rate increase byte count                 : 431068
+  Alpha update G value                     : 512
+  Alpha update interval                    : 1
+  Rate increase in HAI phase               : 300
+  Initial alpha value                      : 64
+  Rate reduce monitor period               : 1
+  Rate increase threshold                  : 1
+  Rate increase interval                   : 1
+  Token bucket size                        : 800000
+  DSCP value used for CNP                  : 46
+
+
+  PFC :
+  PFC priority bitmap       : 0x8
+  PFC no-drop priorities    : 3
+
+  Scheduling :
+  --------------------------------------------
+  Priority  Scheduling  Bandwidth Rate-limit
+            Type        (in %age) (in Gbps)
+  --------------------------------------------
+  0         DWRR        1         N/A
+  3         DWRR        99        N/A
+  6         strict      N/A       10
+  ```
+
+### Configure your network file system (NFS)
+
+Setting up a shared NFS volume facilitates centralized storage for models,
+recipes, and logs across the cluster. Use the following commands to install the
+necessary client tools and mount the remote directory.
+
+```{important}
+Replace `nfs_server_ip:/shared/folder` and `/mount/point` with your specific
+server details and desired local mount path.
+```
+
+``` bash
+sudo apt update && sudo apt install -y nfs-common
+sudo mkdir -p /mount/point
+sudo mount -t nfs nfs_server_ip:/shared/folder /mount/point
+echo "nfs_server_ip:/shared/folder /mount/point nfs _netdev,nofail,x-systemd.automount,x-systemd.idle-timeout=600,vers=4.2 0 0" | sudo tee -a /etc/fstab
+```
+
+## Software installation
+
+Next, install the core compute stack required to operate the AMD Instinct GPUs.
+The following steps guide you through deploying the ROCm software stack and the
+necessary kernel-mode drivers to enable hardware acceleration and optimize the
+environment for distributed inference workloads.
+
+### Install ROCm
+
+Use the following commands to quickly install ROCm 7.1.1 on Ubuntu 22.04:
+
+``` bash
+wget https://repo.radeon.com/amdgpu-install/7.1.1/ubuntu/jammy/amdgpu-install_7.1.1.70101-1_all.deb
+sudo apt install ./amdgpu-install_7.1.1.70101-1_all.deb
+sudo apt update
+sudo apt install python3-setuptools python3-wheel
+sudo usermod -a -G render,video $LOGNAME # Add the current user to the render and video groups
+sudo apt install rocm
+```
+
+For detailed installation instructions, refer to the [ROCm 7.1.1
+documentation](https://rocm.docs.amd.com/projects/install-on-linux/en/docs-7.1.1/install/quick-start.html#rocm-installation).
+
+### Install AMD GPU Driver (amdgpu)
+
+Use the following commands to quickly install the AMD GPU Driver (ROCm 7.1.1)
+on Ubuntu 22.04:
+
+``` bash
+wget https://repo.radeon.com/amdgpu-install/7.1.1/ubuntu/jammy/amdgpu-install_7.1.1.70101-1_all.deb
+sudo apt install ./amdgpu-install_7.1.1.70101-1_all.deb
+sudo apt update
+sudo apt install "linux-headers-$(uname -r)" "linux-modules-extra-$(uname -r)"
+sudo apt install amdgpu-dkms
+```
+
+For detailed installation instructions, refer to the [ROCm 7.1.1
+documentation](https://rocm.docs.amd.com/projects/install-on-linux/en/docs-7.1.1/install/quick-start.html#amdgpu-driver-installation).
+
+## Network verification and testing
+
+Before deploying the inference engine, validate the health and performance of
+the cluster interconnects.
+
+### Verify network connectivity
+
+Verify that all network interfaces are reachable across the cluster nodes.
+Assuming `eth0` is the management interface, and `benic1p1` through `benic8p1` are the
+dedicated RoCE backend interfaces, use the following loop to test reachability
+to a remote node (for instance, a target node with host IP suffix `.38`).
+
+```bash
+# Test connectivity for RoCE subnets 192.168.x.38 (node B) through 192.168.x.37 (node A)
+for i in {1..8}; do ping -c 1 192.168.${i}.38; done
+```
+
+### Validate your RDMA setup
+
+Confirm that all eight RDMA network interfaces are in the `UP` state and
+correctly configured with the required MTU and GID settings.
+
+#### Verify link status MTU, NIC temperature, and NIC speed
+
+```bash
+sudo nicctl show port
+```
+
+The output should look something like this:
+
+```bash
+-------------------------------------------------------------------------------------
+
+NIC  : 42424650-4c32-3531-3530-314343000000 (0000:f6:00.0)
+
+Port : 04908132-5d88-4242-4242-000011010000 (eth1/1)
+  Spec:
+    Ifindex                                  : 0x11010000
+    Type                                     : ETH
+    speed                                    : 400G
+    Admin state                              : UP
+    FEC type                                 : RS
+    Pause type                               : PFC
+    Number of lanes                          : 4
+    MTU                                      : 9216
+    TX pause                                 : enabled
+    RX pause                                 : enabled
+    Auto negotiation                         : enabled
+  Status:
+    Physical port                            : 1
+    Operational status                       : UP
+    Link FSM state                           : UP
+    FEC type                                 : RS
+    Cable type                               : Fiber
+    Number of lanes                          : 4
+    speed                                    : 400G
+    Auto negotiation                         : disabled
+    MAC ID                                   : 0
+    MAC channel                              : 0
+    MAC address                              : 04:90:81:32:5d:88
+    Transceiver type                         : QSFP_CMIS
+    Transceiver state                        : SPROM-READ
+    Transceiver PID                          : QSFP-400G-DR4
+    Transceiver temperature (in C)           : 45
+    Transceiver warning temperature (in C)   : 75
+    Transceiver alarm temperature (in C)     : 80
+-------------------------------------------------------------------------------------
+```
+
+#### Verify GID
+
+Ensure each device has a valid GID mapped to its assigned IP address.
+
+```bash
+ibv_devinfo -v | grep GID
+```
+
+The output should look something like this:
+
+```bash
+      GID[  0]:               fe80::690:81ff:fe30:a7a0, RoCE v2
+      GID[  1]:               ::ffff:192.168.7.36, RoCE v2
+```
+
+### Run RDMA bandwidth benchmarks
+
+Verify the inter-node RDMA performance to ensure the network fabric can
+saturate the link bandwidth.
+
+#### Install RDMA Performance Tools
+
+To get started, build the ROCm-optimized `rdma-perftest` test suite from
+source:
+
+```bash
+sudo apt install -y libibumad-dev libpci-dev libibverbs-dev librdmacm-dev ibverbs-utils libtool
+git clone https://github.com/ROCm/rdma-perftest
+cd rdma-perftest/
+./autogen.sh
+./configure --enable-rocm --with-rocm=/opt/rocm
+make -j$(nproc)
+sudo make install
+```
+
+#### Run a bandwidth test (GPU memory)
+
+Perform a bandwidth test using ROCm GPU memory between two nodes. One acts as
+a server and the other acts as a client. Replace `<SERVER_IP>` with the
+appropriate IP.
+
+```bash
+# On Server Node
+./ib_write_bw --use_rocm=0 -d mlx5_0 --report_gbits -a
+
+# On Client Node
+./ib_write_bw --use_rocm=0 -d mlx5_0 --report_gbits -a <SERVER_IP>
+```
+
+## SGLang serving and MoRI unit tests
+
+### Install Docker Engine
+
+Install the Docker engine to manage the containerized vLLM and MoRI serving
+environments.
+
+```bash
+sudo apt update && sudo apt install -y docker.io
+sudo usermod -aG docker "$USER"
+```
+
+### Launch the serving container
+
+Deploy the SGLang MoRI serving container on each node.
+
+```bash
+CONTAINER_NAME=sglang_mori
+IMAGE_NAME=rocm/sgl-dev:sglang-0.5.6.post1-rocm700-mi35x-mori-0113
+
+docker run -it \
+    --rm \
+    --device /dev/dri --device /dev/kfd --device=/dev/infiniBand \
+    --network host --ipc host \
+    --group-add video \
+    --cap-add SYS_PTRACE \
+    --security-opt seccomp=unconfined \
+    --privileged \
+    --shm-size 128G \
+    --name ${CONTAINER_NAME} \
+    ${IMAGE_NAME} /bin/bash
+```
+
+### Run MoRI inter-node unit tests
+
+Before starting the vLLM service, run the MoRI unit test to verify that the
+inter-node communication backend is correctly configured.
+
+MoRI unit test uses 2 nodes as a minimal validation before running the full
+1P2D (3 nodes) benchmark.
+
+The key configuration variables are:
+
+* `GLOO_SOCKET_IFNAME`: The network interface used for backend initialization such as `eth2`.
+* `<MASTER_IP>`: The IP address of the primary node's backend interface.
+
+```{note}
+You can find reference performance data in the [ROCm/MoRI
+repository](https://github.com/ROCm/mori?tab=readme-ov-file#mori-ep).
+```
+
+```bash
+# Set up environment inside the container
+export PYTHONPATH=/app/mori:$PYTHONPATH
+export GLOO_SOCKET_IFNAME=<BACKEND_INTERFACE>
+
+# Node 0 (Primary)
+torchrun --nnodes=2 --node_rank=0 --nproc_per_node=1 \
+    --master_addr="<MASTER_IP>" --master_port=1234 \
+    examples/ops/dispatch_combine/test_dispatch_combine_internode.py \
+    --cmd bench --kernel-type v1
+
+# Node 1 (Secondary)
+torchrun --nnodes=2 --node_rank=1 --nproc_per_node=1 \
+    --master_addr="<MASTER_IP>" --master_port=1234 \
+    examples/ops/dispatch_combine/test_dispatch_combine_internode.py \
+    --cmd bench --kernel-type v1
+```
+
+## End-to-end 1P2D performance testing
+
+This section guides you through running distributed inference benchmarks using
+the SGLang disagg recipe. For detailed implementation details, refer to the
+[SGLang Disaggregation
+Recipe](https://github.com/billishyahao/sglang_disagg/blob/9n_cluster/README.md).
+
+### Download the model and setup your run environment
+
+This performance test supports the following models:
+
+* [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3)
+* [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1)
+* [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528)
+
+To set up your environment and download the models using the Hugging Face CLI,
+use the following commands. Modify the `huggingface-cli download` command
+to download the desired model.
+
+```bash
+# Set up a virtual environment and install the Hugging Face CLI
+sudo apt update && sudo apt install -y python3-venv
+python3 -m venv ~/venvs/hf
+source ~/venvs/hf/bin/activate
+pip install huggingface_hub
+
+# Download the model to the shared NFS mount point
+# Replace 'deepseek-ai/DeepSeek-R1-0528' with your desired model
+huggingface-cli download --token <your_hf_token> \
+    deepseek-ai/DeepSeek-R1-0528 \
+    --local-dir /mount/point/models/DeepSeek-R1
+```
+
+### Clone the SGLang disaggregation recipe
+
+Clone the SGLang disaggregation repository to the shared file system and switch
+to the appropriate branch:
+
+```bash
+git clone https://github.com/billishyahao/sglang_disagg.git
+git checkout 9n_cluster
+cd sglang_disagg
+```
+
+```{note}
+In the 1P2D configuration, the prefill service and benchmark process run on the
+same node, while remaining nodes handle decode services.
+```
+
+### Configure InfiniBand devices
+
+Identify and configure the available InfiniBand devices.
+
+1. List available devices using the following command.
+
+   ```bash
+   ibv_devinfo -l
+   ```
+
+   Example output:
+
+   ```bash
+   8 HCAs found:
+           ionic_0
+           ionic_1
+           ionic_2
+           ionic_3
+           ionic_4
+           ionic_5
+           ionic_6
+           ionic_7
+   ```
+
+2. Update environment variables. Edit `set_env_vars.sh` and add the
+   comma-separated list of your system's IB devices. For example:
+
+   ```bash
+   export IBDEVICES=ionic_0,ionic_1,ionic_2,ionic_3,ionic_4,ionic_5,ionic_6,ionic_7
+   ```
+
+### Configure the script and submit the job
+
+1. To set the required configuration parameters, update the following
+   environment variables in `run_submit_disagg.sh` to match your cluster setup:
+
+   ```bash
+   # SLURM Job Configuration
+   export SLURM_ACCOUNT="amd"       # The account name for SLURM job accounting and resource allocation
+   export SLURM_PARTITION="compute" # The specific cluster partition (queue) to submit the job to
+   export TIME_LIMIT="24:00:00"     # Maximum wall time for the job (Hours:Minutes:Seconds)
+
+   # Model Configuration
+   export MODEL_PATH="/nfsdata"     # Base directory where the model weights are stored
+   export MODEL_NAME="DeepSeek-R1"  # Specific model directory name (joined with MODEL_PATH)
+   export CONTAINER_IMAGE="rocm/sgl-dev:sglang-0.5.6.post1-rocm700-mi35x-mori-1224" # Docker image to use for the environment
+
+   # Cluster Topology (Disaggregation Setup)
+   export PREFILL_NODES=1           # Number of prefill nodes
+   export PREFILL_WORKERS=1         # Number of prefill workers
+   export DECODE_NODES=2            # Number of decode nodes
+   export DECODE_WORKERS=2          # Number of decode workers
+
+   # Benchmark/Workload Parameters
+   export ISL=1024                  # Input Sequence Length (number of tokens in the prompt)
+   export OSL=1024                  # Output Sequence Length (number of tokens to generate)
+   export CONCURRENCIES="2048"      # Total number of concurrent requests to simulate in the benchmark. The value can be "32,64,128"
+   export REQUEST_RATE="inf"        # Request per second rate. "inf" means send all requests immediately
+
+   # Parallelism Strategies
+   export PREFILL_ENABLE_EP=true    # Enable Expert Parallelism (EP) for the prefill phase
+   export PREFILL_ENABLE_DP=true    # Enable Data Parallelism (DP) for the prefill phase
+   export DECODE_ENABLE_EP=true     # Enable Expert Parallelism (EP) for the decode phase
+   export DECODE_ENABLE_DP=true     # Enable Data Parallelism (DP) for the decode phase
+   ```
+
+2. Then submit the batch job into slurm cluster through `bash ./run_submit_disagg.sh`.
+
+   ```bash
+   bash ./run_submit_disagg.sh
+   ```
+
+### Log file analysis
+
+1. After submission, retrieve the SLURM job ID:
+
+   ```bash
+   squeue
+   ```
+
+   Example output:
+
+   ```bash
+   JOBID PARTITION     NAME     USER ST       TIME  NODES NODELIST(REASON)
+   123   compute       1p2d   alice  R    00:10:32      4 node[01-04]
+   ```
+
+2. A directory named `slurm_job-$SLURM_JOB_ID` is created in `/tmp` on each
+   participating node. The directory contains:
+
+   | Log File | Description |
+   | :--------| :-----------|
+   | `pd_sglang_bench_serving.sh_NODE${NODE_RANK}.log` | Main service log per node |
+   | `decode_NODE${NODE_RANK}.log` | SGLang decode service details |
+   | `prefill_NODE${NODE_RANK}.log` | SGLang prefill service details |
+
+3. The benchmark results will be displayed in
+   `pd_sglang_bench_serving.sh_NODE${NODE_RANK}.log`. Key metrics include:
+
+   ```{note}
+   The following benchmark utility output is provided for reference only and
+   should not be used to compare performance. See the
+   [InferenceMAX](https://inferencemax.semianalysis.com/) website for validated
+   performance results.
+   ```
+
+   ``` bash
+   ============ Serving Benchmark Result ============
+   Successful requests:                     20480
+   Benchmark duration (s):                  1194.25
+   Total input tokens:                      20971520
+   Total generated tokens:                  20971520
+   Request throughput (req/s):              17.15
+   Output token throughput (tok/s):         17560.38
+   Total Token throughput (tok/s):          35120.76
+   ---------------Time to First Token----------------
+   Mean TTFT (ms):                          21601.77
+   Median TTFT (ms):                        24525.21
+   P99 TTFT (ms):                           85417.53
+   -----Time per Output Token (excl. 1st token)------
+   Mean TPOT (ms):                          92.41
+   Median TPOT (ms):                        85.46
+   P99 TPOT (ms):                           138.67
+   ---------------Inter-token Latency----------------
+   Mean ITL (ms):                           92.41
+   Median ITL (ms):                         74.76
+   P99 ITL (ms):                            263.07
+   ----------------End-to-end Latency----------------
+   Mean E2EL (ms):                          116133.48
+   Median E2EL (ms):                        110349.39
+   P99 E2EL (ms):                           227243.97
+   ==================================================
+   ```
+
+## Troubleshooting
+
+The following section outlines common issues and their solutions.
+
+### Bandwidth test fails with error
+
+1. Use ROCm-optimized `rdma-perftest`, not the generic `perftest`
+
+    ```bash
+    which ib_write_bw
+    ```
+
+2. Confirm the `SERVER_IP` is accessible
+
+    ```bash
+    ping <SERVER_IP>
+    ```
+
+3. Check system logs, use `dmesg` for kernel-level errors
+
+    ``` bash
+    sudo dmesg -T | grep -i 'error|warn|fail|exception'
+    ```
+
+### Slurm job fails
+
+Common causes and solutions for Slurm job submission failures include:
+
+1. Shared storage access:
+   * Verify that both `sglang_disagg` and model directories are located in a shared NFS mount accessible to all compute nodes.
+   * Ensure proper permissions: `chmod -R 755 /shared/path/sglang_disagg /shared/path/models`
+
+2. Log analysis:
+   * Examine `pd_sglang_bench_serving.sh_NODE${NODE_RANK}.log` on each participating node for detailed error messages.
+   * Check for common issues like missing dependencies, GPU allocation failures, or network connectivity problems.
+
+3. Configuration validation:
+   * Verify SLURM parameters in `run_submit_disagg.sh`:
+     * `SLURM_ACCOUNT`: Ensure your account has access to the cluster
+     * `SLURM_PARTITION`: Confirm the partition exists and is accessible
+     * `MODEL_PATH`: Check that the path is correct and accessible from compute nodes
+     * `MODEL_NAME`: Verify the model subdirectory exists within `MODEL_PATH`
+   * Use `sinfo` to check partition and node availability.
@@ -0,0 +1,627 @@
+# vLLM distributed inference with MoRI
+
+This document provides a comprehensive guide for setting up a high-performance
+vLLM serving environment on an AMD Instinct MI300X or MI325X GPU cluster using
+the [MoRI (Modular RDMA Interface)](https://github.com/rocm/mori) communication
+backend. It also includes detailed instructions on how to reproduce the
+benchmark results published in the AMD ROCm blog [Practical, Fault-Robust
+Distributed Inference for DeepSeek on AMD
+MI300X](https://rocm.blogs.amd.com/software-tools-optimization/wide-ep-deepseek/README.html).
+
+## Prerequisites
+
+The following hardware configuration is required to implement this setup:
+
+* **Nodes**: A minimum of two GPU nodes (virtual machines or physical machines)
+  for wide expert parallelism (EP) evaluation.
+* **GPUs**: 8x AMD Instinct MI300X/MI325X GPU cards per node.
+* **Networking**: 8x NVIDIA Mellanox ConnectX-7 (CX7) NICs per node, providing
+  a dedicated 1:1 mapping between GPUs and network interfaces for optimal
+  inter-node communication.
+
+## System configuration
+
+This section outlines infrastructure steps required to prepare your cluster for
+high-performance AI workloads. It covers validating your system's software
+baselines and firmware versions, configuring high-bandwidth backend networking
+for inter-node communication, and establish shared storage to ensure
+    a synchronized distributed computing environment.
+
+### Verify baseline software
+
+This setup has been validated using the **AI/ML Ready Image (ROCm 7-based)** on
+Digital Ocean AMD GPU Droplets. The following table outlines the software
+stack versions and appropriate shell commands for verification:
+
+| Component | Version | Verification command |
+| :--- | :--- | :--- |
+| **OS** | Ubuntu 24.04.3 LTS | `cat /etc/os-release` |
+| **Kernel** | 6.8.0-87-generic | `uname -r` |
+| **ROCm** | 7.0.2 | `amd-smi version` |
+| **PLDM bundle (firmware) for MI300X** | 01.25.03.12 | [Verify BKC](#verify-best-known-configuration-bkc) |
+| **PLDM bundle (firmware) for MI325X** | 01.25.03.03 | [Verify BKC](#verify-best-known-configuration-bkc) |
+| **CX7 Firmware** | 28.46.3048 | `dkms status` |
+| **CX7 Driver** | 24.10-3.2.5 | `dkms status` |
+| **DOCA** | 2.9.3 | `dpkg -l \| grep doca` |
+
+### Verify best known configuration (BKC)
+
+The BKC defines a validated configuration of GPU firmware, baseboard firmware,
+ROCm user space components, the AMD GPU Driver, and virtualization tooling.
+These components are tested together to attain best performance and compatibility.
+
+While AMD publishes the AMD GPU driver and ROCm user space components, your
+server OEM or infrastructure provider distributes the firmware packages. AMD
+supplies those firmware images (PLDM bundles), which the OEM integrates and
+distributes.
+
+To verify the active BKC and IFWI (Integrated Firmware Image) versions via the
+Redfish API:
+
+1. Prepare credentials: Identify your BMC IP, username, and password.
+2. Run Redfish queries: Use the following commands to check the active
+   firmware inventory.
+
+     ``` bash
+     # Define BMC connection variables
+     BMC_IP="<BMC_IP>"
+     AUTH="<username>:<password>"
+
+     # Query active BKC bundle version
+     curl -X GET "https://${BMC_IP}/redfish/v1/UpdateService/FirmwareInventory/bundle_active" \
+          -u "${AUTH}" -k | json_pp
+
+     # Query active IFWI (Integrated Firmware Image)
+     curl -X GET "https://${BMC_IP}/redfish/v1/UpdateService/FirmwareInventory/firmware_active" \
+          -u "${AUTH}" -k | json_pp
+     ```
+
+### Run basic system health checks
+
+Before proceeding with software deployment, verify that all cluster nodes
+comply with the [MI300X Basic Health
+Checks](https://instinct.docs.amd.com/projects/system-acceptance/en/latest/gpus/mi300x.html#basic-health-checks)
+or [MI325X Basic Health
+Checks](https://instinct.docs.amd.com/projects/system-acceptance/en/latest/gpus/mi325x.html#basic-health-checks).
+Key requirements include specific kernel boot arguments, minimum system memory
+thresholds, PCIe Gen5 link stability, and so on.
+
+### Configure your backend network (netplan)
+
+Configure the backend NICs for high-bandwidth inter-node communication. Suppose
+the GPU’s eight network interface controllers (NICs) are eth2 to eth9. Each NIC
+must have its own subnet that is disjoint from the others. For example, `eth2`
+could use `192.168.50.0/24`, `eth3` could use `192.168.51.0/24`, and so on.
+Each node needs a unique IP address on each subnet. You should use the same
+final octet in each subnet for a given node. For example, one node would have
+the addresses `192.168.50.2`, `192.168.51.2`, and so on. Another node might
+have `192.168.50.3`, `192.168.51.3`, and so on. Ensure MTU is set to `4200`.
+
+```{note}
+Ensure you identify the correct interface names for your system using ip link
+before applying this configuration.
+```
+
+For example, your `/etc/netplan/50-backend.yaml` might include something like
+the following:
+
+```yaml
+eth2:
+  dhcp4: false
+  dhcp6: false
+  link-local: []
+  addresses:
+    - 192.168.50.2/24
+  mtu: 4200
+eth3:
+  dhcp4: false
+  dhcp6: false
+  link-local: []
+  addresses:
+    - 192.168.51.2/24
+  mtu: 4200
+eth4:
+  dhcp4: false
+  dhcp6: false
+  link-local: []
+  addresses:
+    - 192.168.52.2/24
+  mtu: 4200
+eth5:
+  dhcp4: false
+  dhcp6: false
+  link-local: []
+  addresses:
+    - 192.168.53.2/24
+  mtu: 4200
+eth6:
+  dhcp4: false
+  dhcp6: false
+  link-local: []
+  addresses:
+    - 192.168.54.2/24
+  mtu: 4200
+eth7:
+  dhcp4: false
+  dhcp6: false
+  link-local: []
+  addresses:
+    - 192.168.55.2/24
+  mtu: 4200
+eth8:
+  dhcp4: false
+  dhcp6: false
+  link-local: []
+  addresses:
+    - 192.168.56.2/24
+  mtu: 4200
+eth9:
+  dhcp4: false
+  dhcp6: false
+  link-local: []
+  addresses:
+    - 192.168.57.2/24
+  mtu: 4200
+```
+
+To apply the configuration, use the following command.
+
+```bash
+sudo netplan apply
+```
+
+To verify your configuration, use the following command.
+
+```bash
+sudo apt install -y net-tools && ip -br a
+```
+
+### Configure your network file system (NFS)
+
+Setting up a shared NFS volume facilitates centralized storage for models,
+recipes, and logs across the cluster. Use the following commands to install the
+necessary client tools and mount the remote directory.
+
+```{important}
+Replace `nfs_server_ip:/shared/folder` and `/mount/point` with your specific
+server details and desired local mount path.
+```
+
+``` bash
+sudo apt update && sudo apt install -y nfs-common
+sudo mkdir -p /mount/point
+sudo mount -t nfs nfs_server_ip:/shared/folder /mount/point
+echo "nfs_server_ip:/shared/folder /mount/point nfs _netdev,nofail,x-systemd.automount,x-systemd.idle-timeout=600,vers=4.2 0 0" | sudo tee -a /etc/fstab
+```
+
+### Configure static hostname resolution for backend initialization (optional)
+
+If the high-speed RDMA/IB interfaces are used for the initial distributed
+coordination (such as `MASTER_ADDR`), you must configure static hostname
+resolution. This ensures that cluster host names resolve to the backend network
+IPs rather than the management or local loopback addresses.
+
+Follow these steps to configure static hostname resolution:
+
+1. Edit `/etc/hosts` on all nodes: for example, using `sudo vim /etc/hosts`.
+2. Add the backend IP and hostname mappings.
+3. Comment out any default local mappings (such as `127.0.1.1`) for the current
+   hostname to avoid resolution conflicts.
+
+For example, your `/etc/hosts` entries might look like:
+
+```text
+# Map host names to backend network IPs
+192.168.50.2 mori_test_01
+192.168.50.3 mori_test_02
+
+# Comment out the default entry to ensure resolution via the backend IP
+# 127.0.1.1 mori_test_01 mori_test_01
+```
+
+## Software installation
+
+Next, install the essential software stack required to operate the AMD Instinct
+GPUs and high-speed networking components. Follow these steps to deploy the
+NVIDIA DOCA drivers for Mellanox ConnectX-7 NICs, the ROCm software stack, and
+the necessary kernel modules to enable hardware acceleration.
+
+### Install CX7 driver and firmware
+
+1. Download and install the `DOCA 2.9.3` driver following the instructions in
+   [NVIDIA DOCA 2.9.3
+   Downloads](https://developer.nvidia.com/doca-2-9-3-download-archive?deployment_platform=Host-Server&deployment_package=DOCA-Host&target_os=Linux&Architecture=x86_64&Profile=doca-all&Distribution=Ubuntu&version=24.04&installer_type=deb_local).
+
+2. Download the appropriate firmware for your hardware PSID from the [NVIDIA
+   official website](https://network.nvidia.com/support/firmware/connectx7/)
+   and flash the device.
+
+3. To verify driver and firmware versions, use the following command. Replace
+   `IB Device` with your specific backend interface.
+
+   ```bash
+   ethtool -i <IB Device>
+   ```
+
+### Install ROCm
+
+Use the following commands to quickly install ROCm 7.0.2 on Ubuntu 24.04:
+
+``` bash
+wget https://repo.radeon.com/amdgpu-install/7.0.2/ubuntu/noble/amdgpu-install_7.0.2.70002-1_all.deb
+sudo apt install ./amdgpu-install_7.0.2.70002-1_all.deb
+sudo apt update
+sudo apt install python3-setuptools python3-wheel
+sudo usermod -a -G render,video $LOGNAME # Add the current user to the render and video groups
+sudo apt install rocm
+```
+
+For detailed installation instructions, refer to the [ROCm 7.0.2
+documentation](https://rocm.docs.amd.com/projects/install-on-linux/en/docs-7.0.2/install/quick-start.html#rocm-installation).
+
+### Install AMD GPU Driver (amdgpu)
+
+Use the following commands to quickly install the AMD GPU Driver (ROCm 7.0.2) on Ubuntu 24.04:
+
+``` bash
+wget https://repo.radeon.com/amdgpu-install/7.0.2/ubuntu/noble/amdgpu-install_7.0.2.70002-1_all.deb
+sudo apt install ./amdgpu-install_7.0.2.70002-1_all.deb
+sudo apt update
+sudo apt install "linux-headers-$(uname -r)" "linux-modules-extra-$(uname -r)"
+sudo apt install amdgpu-dkms
+```
+
+For detailed installation instructions, refer to the [ROCm 7.0.2
+documentation](https://rocm.docs.amd.com/projects/install-on-linux/en/docs-7.0.2/install/quick-start.html#amdgpu-driver-installation).
+
+## Network verification and testing
+
+Before deploying the inference engine, validate the health and performance of
+the cluster interconnects.
+
+### Verify network connectivity
+
+Verify that all network interfaces are reachable across the cluster nodes.
+Assuming `eth0` is the management interface, `eth1` is for the VPC, and `eth2`
+through `eth9` are the dedicated RoCE backend interfaces, use the following
+loop to test reachability to a remote node (for instance, a target node with
+host IP suffix `.3`).
+
+```bash
+# Test connectivity for RoCE subnets 192.168.50.x through 192.168.57.x
+for i in {0..7}; do ping -c 1 192.168.5${i}.3; done
+```
+
+### Validate your RDMA setup
+
+Confirm that all eight RDMA network interfaces are in `UP` state. Verify the MTU
+setting of `4096` and ensure each device has a valid GID mapped to its assigned
+IP address.
+
+``` bash
+ibv_devinfo -v
+```
+
+The output should look something like this:
+
+``` bash
+hca_id: mlx5_0
+        transport:                      InfiniBand (0)
+        fw_ver:                         28.46.3048
+        ...
+        board_id:                       MT_0000000838
+        phys_port_cnt:                  1
+                port:   1
+                        state:                  PORT_ACTIVE (4)
+                        max_mtu:                4096 (5)
+                        active_mtu:             4096 (5)
+                        sm_lid:                 0
+                        port_lid:               0
+                        port_lmc:               0x00
+                        link_layer:             Ethernet
+                        ...
+                        GID[  0]:               fe80:0000:0000:0000:d894:24ff:fe4a:96e2, RoCE v1
+                        GID[  1]:               fe80::d894:24ff:fe4a:96e2, RoCE v2
+                        GID[  2]:               0000:0000:0000:0000:0000:ffff:c0a8:3903, RoCE v1
+                        GID[  3]:               ::ffff:192.168.57.3, RoCE v2
+```
+
+### Run RDMA bandwidth benchmarks
+
+Verify the inter-node RDMA performance to ensure the network fabric can
+saturate the link bandwidth.
+
+#### Install RDMA Performance Tools
+
+To get started, build the ROCm-optimized `rdma-perftest` test suite from
+source:
+
+```bash
+sudo apt install -y libibumad-dev libpci-dev libibverbs-dev librdmacm-dev ibverbs-utils libtool
+git clone https://github.com/ROCm/rdma-perftest
+cd rdma-perftest/
+./autogen.sh
+./configure --enable-rocm --with-rocm=/opt/rocm
+make -j$(nproc)
+sudo make install
+```
+
+#### Run a bandwidth test (GPU memory)
+
+Perform a bandwidth test using ROCm GPU memory between two nodes. One acts
+as a server and the other acts as a client. For 400G interfaces, the expected
+peak throughput is approximately 390 Gbps. Replace `<SERVER_IP>` with the
+appropriate IP.
+
+```bash
+# On Server Node
+./ib_write_bw --use_rocm=0 -d mlx5_0 --report_gbits -a
+
+# On Client Node
+./ib_write_bw --use_rocm=0 -d mlx5_0 --report_gbits -a <SERVER_IP>
+```
+
+## vLLM serving and MoRI unit tests
+
+### Install Docker Engine
+
+Install the Docker engine to manage the containerized vLLM and MoRI serving
+environments.
+
+```bash
+sudo apt update && sudo apt install -y docker.io
+```
+
+### Download the DeepSeek PTPC model
+
+This guide uses the
+[DeepSeek-R1-FP8-Dynamic](https://huggingface.co/EmbeddedLLM/deepseek-r1-FP8-Dynamic)
+model optimized for PTPC. Use the following commands to install the Hugging
+Face CLI and download the model to your shared NFS directory:
+
+```bash
+# Set up a virtual environment and install the Hugging Face CLI
+sudo apt update && sudo apt install -y python3-venv
+python3 -m venv ~/venvs/hf
+source ~/venvs/hf/bin/activate
+pip install huggingface_hub
+
+# Download the model to the shared NFS mount point
+huggingface-cli download --token <your_hf_token> \
+    EmbeddedLLM/deepseek-r1-FP8-Dynamic \
+    --local-dir /mount/point/models/EmbeddedLLM/deepseek-r1-FP8-Dynamic
+```
+
+### Launch the serving container
+
+Deploy the vLLM MoRI serving Docker container on each node.
+
+```bash
+CONTAINER_NAME=vllm_mori
+IMAGE_NAME=aigmkt/vllm:mori_rocm6.4.1_20251105
+
+docker run -it \
+    --rm \
+    --device /dev/dri --device /dev/kfd --device=/dev/infiniBand \
+    --network host --ipc host \
+    --group-add video \
+    --cap-add SYS_PTRACE \
+    --security-opt seccomp=unconfined \
+    --privileged \
+    -v /mount/point/models:/models \
+    --shm-size 128G \
+    --name ${CONTAINER_NAME} \
+    ${IMAGE_NAME} /bin/bash
+```
+
+### Run MoRI inter-node unit tests
+
+Before starting the vLLM service, run the MoRI unit test to verify that the
+inter-node communication backend is correctly configured.
+
+The key configuration variables are:
+
+* `GLOO_SOCKET_IFNAME`: The network interface used for backend initialization such as `eth2`.
+* `<MASTER_IP>`: The IP address of the primary node's backend interface.
+
+```{note}
+You can find reference performance data in the [ROCm/MoRI
+repository](https://github.com/ROCm/mori?tab=readme-ov-file#mori-ep).
+```
+
+```bash
+# Set up environment inside the container
+cd /app/mori
+export PYTHONPATH=/app/mori:$PYTHONPATH
+export GLOO_SOCKET_IFNAME=<BACKEND_INTERFACE>
+
+# Node 0 (Primary)
+torchrun --nnodes=2 --node_rank=0 --nproc_per_node=1 \
+    --master_addr="<MASTER_IP>" --master_port=1234 \
+    examples/ops/dispatch_combine/test_dispatch_combine_internode.py \
+    --cmd bench --kernel-type v1
+
+# Node 1 (Secondary)
+torchrun --nnodes=2 --node_rank=1 --nproc_per_node=1 \
+    --master_addr="<MASTER_IP>" --master_port=1234 \
+    examples/ops/dispatch_combine/test_dispatch_combine_internode.py \
+    --cmd bench --kernel-type v1
+```
+
+### Deploy and serve the model
+
+To deploy DeepSeek-R1 (PTPC) with Expert Parallelism 16 (EP16) across two
+nodes, use the following serving scripts.
+
+#### Create serving scripts
+
+Create the following scripts inside the container on each node.
+
+* Node 0 (master node): `ep16_node0.sh`
+
+   ```bash
+   #!/bin/bash
+
+   # Add VLLM_ENFORCE_EPLB=1 to enforce EP balance
+   export VLLM_ROCM_USE_AITER=1
+   export VLLM_ROCM_USE_AITER_MOE=1
+   export VLLM_LOGGING_LEVEL=INFO
+   export VLLM_USE_V1=1
+   export VLLM_ROCM_USE_AITER_MLA=1
+   export VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS=0
+   export VLLM_ALL2ALL_BACKEND=mori
+
+   vllm serve /models/EmbeddedLLM/deepseek-r1-FP8-Dynamic/ \
+       -dp 16 \
+       --enable-expert-parallel \
+       --data-parallel-size-local 8 \
+       --data-parallel-address ${IP} \
+       --data-parallel-rpc-port 1212 \
+       --served-model-name deepseek \
+       --port 8777 \
+       --block-size 1 \
+       --distributed-executor-backend mp \
+       --gpu-memory-utilization 0.8 \
+       --max-model-len 8192 \
+       --max-num-batched-tokens 4096 \
+       --max-num-seqs 4096 \
+       --compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY", "custom_ops": ["+quant_fp8"]}' \
+       --cuda-graph-sizes 1 2 4 8 16 32 64 128 256 \
+       --kv-cache-dtype fp8 \
+       --no-enable-prefix-caching \
+       --trust-remote-code 2>&1 | tee serving_node0_ep16.log
+    ```
+
+* Node 1: `ep16_node1.sh`
+
+   ```bash
+   #!/bin/bash
+
+   # Add VLLM_ENFORCE_EPLB=1 to enforce EP balance
+   export VLLM_ROCM_USE_AITER=1
+   export VLLM_ROCM_USE_AITER_MOE=1
+   export VLLM_LOGGING_LEVEL=INFO
+   export VLLM_USE_V1=1
+   export VLLM_ROCM_USE_AITER_MLA=1
+   export VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS=0
+   export VLLM_ALL2ALL_BACKEND=mori
+
+   vllm serve /models/EmbeddedLLM/deepseek-r1-FP8-Dynamic/ \
+           -dp 16 \
+           --enable-expert-parallel \
+           --headless \
+           --data-parallel-size-local 8 \
+           --data-parallel-start-rank 8 \
+           --data-parallel-address ${IP} \
+           --data-parallel-rpc-port 1212 \
+           --served-model-name deepseek \
+           --port 8777 \
+           --block-size 1 \
+           --distributed-executor-backend mp \
+           --gpu_memory_utilization 0.8 \
+           --max-model-len 8192 \
+           --max_num_batched_token 4096 \
+           --max-num-seqs 4096 \
+           --compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY", "custom_ops": ["+quant_fp8"]}' \
+           --cuda-graph-sizes 1 2 4 8 16 32 64 128 256 \
+           --kv-cache-dtype fp8 \
+           --no-enable-prefix-caching \
+           --trust-remote-code 2>&1 | tee serving_node1_ep16.log
+    ```
+
+#### Run the serving scripts
+
+Run the scripts on each node to launch the distributed serving instance.
+Replace `<MASTER_IP>` with the backend network IP of Node 0.
+
+```bash
+# On Node 0 (Primary)
+export NCCL_SOCKET_IFNAME=<BACKEND_INTERFACE>
+export GLOO_SOCKET_IFNAME=<BACKEND_INTERFACE>
+IP=<MASTER_IP> bash ep16_node0.sh
+
+# On Node 1 (Secondary)
+export NCCL_SOCKET_IFNAME=<BACKEND_INTERFACE>
+export GLOO_SOCKET_IFNAME=<BACKEND_INTERFACE>
+IP=<MASTER_IP> bash ep16_node1.sh
+```
+
+## Reproducing performance
+
+This section details how to reproduce the performance metrics published in the
+AMD ROCm Blog: [Practical, Fault-Robust Distributed Inference for DeepSeek on
+AMD
+MI300X](https://rocm.blogs.amd.com/software-tools-optimization/wide-ep-deepseek/README.html).
+
+### Configuration for EP16 (16 GPUs)
+
+To achieve the reported throughput, expert parallelism 16 (EP16) is used across
+the decode nodes.
+
+#### Benchmark target
+
+* Decode throughput: ~12.4k output tokens/s per node.
+
+### Performance reproduction commands
+
+Use the following configurations to reproduce published performance metrics.
+
+#### Decode benchmark
+
+To reproduce the 12.4k output tokens/s, use the following configuration:
+
+```bash
+#!/bin/bash
+
+MAX_CONCURRENCY=${1:-3072}
+TIMES=2
+NUM_PROMPTS=$((MAX_CONCURRENCY*TIMES))
+vllm bench serve \
+    --max-concurrency $MAX_CONCURRENCY \
+    --num-prompts $NUM_PROMPTS \
+    --model /models/EmbeddedLLM/deepseek-r1-FP8-Dynamic/ \
+    --served-model-name deepseek \
+    --port 8777 \
+    --ignore-eos \
+    --trust-remote-code \
+    --dataset-name random \
+    --seed 2025 \
+    --random-input-len 2048 \
+    --random-output-len 1024 2>&1 | tee bench_decode_${MAX_CONCURRENCY}_isl_2k_osl_1k.log
+```
+
+To calculate the per-node throughput for comparison with the blog data, take
+the reported **Peak output token throughput (tok/s)** from the benchmark
+results and divide it by the total number of nodes in the cluster.
+
+## Troubleshooting
+
+The following section outlines common issues and their solutions.
+
+### Bandwidth test fails with error
+
+1. Use ROCm-optimized `rdma-perftest`, not the generic `perftest`.
+
+    ``` bash
+    which ib_write_bw
+    ```
+
+2. Confirm the `SERVER_IP` is accessible.
+
+    ``` bash
+    ping <SERVER_IP>
+    ```
+
+3. Check system logs, use `dmesg` for kernel-level errors.
+
+    ``` bash
+    sudo dmesg -T | grep -i 'error|warn|fail|exception'
+    ```
+
+### vLLM EP 16 with MoRI backend fails to launch
+
+1. Error: `Waiting for init message from front-end.` Check the connectivity of the `IP`. Disable firewall/selinux or allow traffic for port `1212`.
+
+2. Verify server name resolution. Ensure server names are correctly mapped in `/etc/hosts`.
+
+3. Confirm whether environment variable `GLOO_SOCKET_IFNAME` is set before running the vLLM serving script.
@@ -2,474 +2,65 @@
   :description: Learn how to validate LLM inference performance on MI300X GPUs using AMD MAD and the ROCm vLLM Docker image.
   :keywords: model, MAD, automation, dashboarding, validate

-**********************************
-vLLM inference performance testing
-**********************************
+**************
+vLLM inference
+**************

-.. _vllm-benchmark-unified-docker-930:
+The `ROCm-enabled vLLM Docker image
+<https://hub.docker.com/r/vllm/vllm-openai-rocm/tags>`__ offers a prebuilt,
+optimized environment for large language model (LLM) inference on AMD Instinct
+MI355X, MI350X, MI325X and MI300X GPUs. This ROCm vLLM Docker image integrates
+vLLM and PyTorch tailored specifically for AMD Instinct data center GPUs.

-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/vllm-benchmark-models.yaml
-
-   {% set docker = data.dockers[0] %}
-
-   The `ROCm vLLM Docker <{{ docker.docker_hub_url }}>`_ image offers a
-   prebuilt, optimized environment for validating large language model (LLM)
-   inference performance on AMD Instinct™ MI355X, MI350X, MI325X and MI300X
-   GPUs. This ROCm vLLM Docker image integrates vLLM and PyTorch tailored
-   specifically for AMD data center GPUs and includes the following components:
-
-   .. tab-set::
-
-      .. tab-item:: {{ docker.pull_tag }}
-
-         .. list-table::
-            :header-rows: 1
-
-            * - Software component
-              - Version
-
-            {% for component_name, component_version in docker.components.items() %}
-            * - {{ component_name }}
-              - {{ component_version }}
-            {% endfor %}
-
-With this Docker image, you can quickly test the :ref:`expected
-inference performance numbers <vllm-benchmark-performance-measurements-930>` for
-AMD Instinct GPUs.
+This container integrates ROCm, PyTorch, and vLLM with optimizations tailored
+for AMD Instinct data center GPUs, enabling consistent and reproducible
+inference deployments.

 What's new
 ==========

-The following is summary of notable changes since the :doc:`previous ROCm/vLLM Docker release <previous-versions/vllm-history>`.
+- For vLLM release notes on model support, hardware and performance improvements,
+  and other highlights, see the `vLLM Releases page
+  <https://github.com/vllm-project/vllm/releases>`__ on GitHub.

-* Added support for AMD Instinct MI355X and MI350X GPUs.
+- It's now recommended to use the upstream vLLM documentation at `docs.vllm.ai
+  <https://docs.vllm.ai>`__ for the latest inference and deployment guides.

-* Added support and benchmarking instructions for the following models. See :ref:`vllm-benchmark-supported-models-930`.
+Get started
+===========

-  * Llama 4 Scout and Maverick
+For a consistent and portable inference environment, it's recommended to use Docker. vLLM
+offers a Docker image `vllm/vllm-openai-rocm
+<https://hub.docker.com/r/vllm/vllm-openai-rocm/tags>`__ for deployment on AMD
+GPUs. Use the following command to pull the latest Docker image from Docker Hub.

-  * DeepSeek R1 0528 FP8
+.. code-block:: shell

-  * MXFP4 models (MI355X and MI350X only): Llama 3.3 70B MXFP4 and Llama 3.1 405B MXFP4
+   docker pull vllm/vllm-openai-rocm:latest

-  * GPT OSS 20B and 120B
-
-  * Qwen 3 32B, 30B-A3B, and 235B-A22B
-
-* Removed the deprecated ``--max-seq-len-to-capture`` flag.
-
-* ``--gpu-memory-utilization`` is now configurable via the `configuration files
-  <https://github.com/ROCm/MAD/tree/develop/scripts/vllm/configs>`__ in the MAD
-  repository.
-
-.. _vllm-benchmark-supported-models-930:
-
-Supported models
-================
-
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/vllm-benchmark-models.yaml
-
-   {% set docker = data.dockers[0] %}
-   {% set model_groups = data.model_groups %}
-
-   .. _vllm-benchmark-available-models-930:
-
-   The following models are supported for inference performance benchmarking
-   with vLLM and ROCm. Some instructions, commands, and recommendations in this
-   documentation might vary by model -- select one to get started. MXFP4 models
-   are only supported on MI355X and MI350X GPUs.
-
-   .. raw:: html
-
-      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
-         <div class="row gx-0">
-            <div class="col-2 me-1 px-2 model-param-head">Model</div>
-            <div class="row col-10 pe-0">
-      {% for model_group in model_groups %}
-               <div class="col-4 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
-      {% endfor %}
-            </div>
-         </div>
-
-         <div class="row gx-0 pt-1">
-            <div class="col-2 me-1 px-2 model-param-head">Variant</div>
-            <div class="row col-10 pe-0">
-      {% for model_group in model_groups %}
-         {% set models = model_group.models %}
-         {% for model in models %}
-            {% if models|length % 3 == 0 %}
-               <div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
-            {% else %}
-               <div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
-            {% endif %}
-         {% endfor %}
-      {% endfor %}
-            </div>
-         </div>
-      </div>
-
-   .. _vllm-benchmark-vllm-930:
-
-   {% for model_group in model_groups %}
-      {% for model in model_group.models %}
-
-   .. container:: model-doc {{ model.mad_tag }}
-
-
-      {% if model.precision == "float4" %}
-      .. important::
-
-         MXFP4 is supported only on MI355X and MI350X GPUs.
-      {% endif %}
-
-      .. note::
-
-         See the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`_ to learn more about your selected model.
-         Some models require access authorization prior to use via an external license agreement through a third party.
-      {% if model.precision == "float8" and model.model_repo.startswith("amd") %}
-         This model uses FP8 quantization via `AMD Quark <https://quark.docs.amd.com/latest/>`__ for efficient inference on AMD GPUs.
-      {% endif %}
-      {% if model.precision == "float4" and model.model_repo.startswith("amd") %}
-         This model uses FP4 quantization via `AMD Quark <https://quark.docs.amd.com/latest/>`__ for efficient inference on AMD GPUs.
-      {% endif %}
-
-      {% endfor %}
-   {% endfor %}
-
-.. _vllm-benchmark-performance-measurements-930:
-
-Performance measurements
-========================
-
-To evaluate performance, the
-`Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
-page provides reference throughput and serving measurements for inferencing popular AI models.
-
-.. important::
-
-   The performance data presented in
-   `Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
-   only reflects the latest version of this inference benchmarking environment.
-   The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct GPUs or ROCm software.
-
-System validation
-=================
-
-Before running AI workloads, it's important to validate that your AMD hardware is configured
-correctly and performing optimally.
-
-If you have already validated your system settings, including aspects like NUMA auto-balancing, you
-can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
-optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
-before starting training.
-
-To test for optimal performance, consult the recommended :ref:`System health benchmarks
-<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
-system's configuration.
-
-Pull the Docker image
-=====================
-
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/vllm-benchmark-models.yaml
-
-   {% set docker = data.dockers[0] %}
-
-   Download the `ROCm vLLM Docker image <{{ docker.docker_hub_url }}>`_.
-   Use the following command to pull the Docker image from Docker Hub.
-
-   .. code-block:: shell
-
-      docker pull {{ docker.pull_tag }}
-
-Benchmarking
-============
-
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/vllm-benchmark-models.yaml
-
-   {% set docker = data.dockers[0] %}
-   {% set model_groups = data.model_groups %}
-
-   Once the setup is complete, choose between two options to reproduce the
-   benchmark results:
-
-   .. _vllm-benchmark-mad-930:
-
-   {% for model_group in model_groups %}
-      {% for model in model_group.models %}
-
-   .. container:: model-doc {{model.mad_tag}}
-
-      .. tab-set::
-
-         .. tab-item:: MAD-integrated benchmarking
-
-            The following run command is tailored to {{ model.model }}.
-            See :ref:`vllm-benchmark-supported-models-930` to switch to another available model.
-
-            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
-               directory and install the required packages on the host machine.
-
-               .. code-block:: shell
-
-                  git clone https://github.com/ROCm/MAD
-                  cd MAD
-                  pip install -r requirements.txt
-
-            2. On the host machine, use this command to run the performance benchmark test on
-               the `{{model.model}} <{{ model.url }}>`_ model using one node with the
-               :literal:`{{model.precision}}` data type.
-
-               .. code-block:: shell
-
-                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
-                  madengine run \
-                      --tags {{model.mad_tag}} \
-                      --keep-model-dir \
-                      --live-output
-
-            MAD launches a Docker container with the name
-            ``container_ci-{{model.mad_tag}}``. The throughput and serving reports of the
-            model are collected in the following paths: ``{{ model.mad_tag }}_throughput.csv``
-            and ``{{ model.mad_tag }}_serving.csv``.
-
-            Although the :ref:`available models
-            <vllm-benchmark-available-models-930>` are preconfigured to collect
-            offline throughput and online serving performance data, you can
-            also change the benchmarking parameters. See the standalone
-            benchmarking tab for more information.
-
-            {% if model.tunableop %}
-
-            .. note::
-
-               For improved performance, consider enabling :ref:`PyTorch TunableOp <mi300x-tunableop>`.
-               TunableOp automatically explores different implementations and configurations of certain PyTorch
-               operators to find the fastest one for your hardware.
-
-               By default, ``{{model.mad_tag}}`` runs with TunableOp disabled (see
-               `<https://github.com/ROCm/MAD/blob/develop/models.json>`__). To enable it, include
-               the ``--tunableop on`` argument in your run.
-
-               Enabling TunableOp triggers a two-pass run -- a warm-up followed by the
-               performance-collection run.
-
-            {% endif %}
-
-         .. tab-item:: Standalone benchmarking
-
-            The following commands are optimized for {{ model.model }}.
-            See :ref:`vllm-benchmark-supported-models-930` to switch to another available model.
-
-            .. seealso::
-
-               For more information on configuration, see the `config files
-               <https://github.com/ROCm/MAD/tree/develop/scripts/vllm/configs>`__
-               in the MAD repository. Refer to the `vLLM engine <https://docs.vllm.ai/en/latest/configuration/engine_args.html#engineargs>`__
-               for descriptions of available configuration options
-               and `Benchmarking vLLM <https://github.com/vllm-project/vllm/blob/main/benchmarks/README.md>`__ for
-               additional benchmarking information.
-
-            .. rubric:: Launch the container
-
-            You can run the vLLM benchmark tool independently by starting the
-            `Docker container <{{ docker.docker_hub_url }}>`_ as shown
-            in the following snippet.
-
-            .. code-block:: shell
-
-               docker pull {{ docker.pull_tag }}
-               docker run -it \
-                   --device=/dev/kfd \
-                   --device=/dev/dri \
-                   --group-add video \
-                   --shm-size 16G \
-                   --security-opt seccomp=unconfined \
-                   --security-opt apparmor=unconfined \
-                   --cap-add=SYS_PTRACE \
-                   -v $(pwd):/workspace \
-                   --env HUGGINGFACE_HUB_CACHE=/workspace \
-                   --name test \
-                   {{ docker.pull_tag }}
-
-            .. rubric:: Throughput command
-
-            Use the following command to start the throughput benchmark.
-
-            .. code-block:: shell
-
-               model={{ model.model_repo }}
-               tp={{ model.config.tp }}
-               num_prompts={{ model.config.num_prompts | default(1024) }}
-               in={{ model.config.in | default(128) }}
-               out={{ model.config.in | default(128) }}
-               dtype={{ model.config.dtype | default("auto") }}
-               kv_cache_dtype={{ model.config.kv_cache_dtype }}
-               max_num_seqs={{ model.config.max_num_seqs | default(1024) }}
-               max_num_batched_tokens={{ model.config.max_num_batched_tokens }}
-               max_model_len={{ model.config.max_model_len }}
-
-               vllm bench throughput --model $model \
-                   -tp $tp \
-                   --num-prompts $num_prompts \
-                   --input-len $in \
-                   --output-len $out \
-                   --dtype $dtype \
-                   --kv-cache-dtype $kv_cache_dtype \
-                   --max-num-seqs $max_num_seqs \
-                   --max-num-batched-tokens $max_num_batched_tokens \
-                   --max-model-len $max_model_len \
-                   --trust-remote-code \
-                   --output-json ${model}_throughput.json \
-                   --gpu-memory-utilization {{ model.config.gpu_memory_utilization | default(0.9) }}
-
-            .. rubric:: Serving command
-
-            1. Start the server using the following command:
-
-               .. code-block:: shell
-
-                  model={{ model.model_repo }}
-                  tp={{ model.config.tp }}
-                  dtype={{ model.config.dtype }}
-                  kv_cache_dtype={{ model.config.kv_cache_dtype }}
-                  max_num_seqs=256
-                  max_num_batched_tokens={{ model.config.max_num_batched_tokens }}
-                  max_model_len={{ model.config.max_model_len }}
-
-                  vllm serve $model \
-                      -tp $tp \
-                      --dtype $dtype \
-                      --kv-cache-dtype $kv_cache_dtype \
-                      --max-num-seqs $max_num_seqs \
-                      --max-num-batched-tokens $max_num_batched_tokens \
-                      --max-model-len $max_model_len \
-                      --no-enable-prefix-caching \
-                      --swap-space 16 \
-                      --disable-log-requests \
-                      --trust-remote-code \
-                      --gpu-memory-utilization 0.9
-
-               Wait until the model has loaded and the server is ready to accept requests.
-
-            2. On another terminal on the same machine, run the benchmark:
-
-               .. code-block:: shell
-
-                  # Connect to the container
-                  docker exec -it test bash
-
-                  # Wait for the server to start
-                  until curl -s http://localhost:8000/v1/models; do sleep 30; done
-
-                  # Run the benchmark
-                  model={{ model.model_repo }}
-                  max_concurrency=1
-                  num_prompts=10
-                  in=128
-                  out=128
-                  vllm bench serve --model $model \
-                      --percentile-metrics "ttft,tpot,itl,e2el" \
-                      --dataset-name random \
-                      --ignore-eos \
-                      --max-concurrency $max_concurrency \
-                      --num-prompts $num_prompts \
-                      --random-input-len $in \
-                      --random-output-len $out \
-                      --trust-remote-code \
-                      --save-result \
-                      --result-filename ${model}_serving.json
-
-            .. note::
-
-               For improved performance with certain Mixture of Experts models, such as Mixtral 8x22B,
-               try adding ``export VLLM_ROCM_USE_AITER=1`` to your commands.
-
-               If you encounter the following error, pass your access-authorized Hugging
-               Face token to the gated models.
-
-               .. code-block::
-
-                  OSError: You are trying to access a gated repo.
-
-                  # pass your HF_TOKEN
-                  export HF_TOKEN=$your_personal_hf_token
-
-            .. raw:: html
-
-               <style>
-               mjx-container[jax="CHTML"][display="true"] {
-                  text-align: left;
-                  margin: 0;
-               }
-               </style>
-
-            .. note::
-
-               Throughput is calculated as:
-
-               - .. math:: throughput\_tot = requests \times (\mathsf{\text{input lengths}} + \mathsf{\text{output lengths}}) / elapsed\_time
-
-               - .. math:: throughput\_gen = requests \times \mathsf{\text{output lengths}} / elapsed\_time
-      {% endfor %}
-   {% endfor %}
-
-Advanced usage
-==============
-
-For information on experimental features and known issues related to ROCm optimization efforts on vLLM,
-see the developer's guide at `<https://github.com/ROCm/vllm/blob/documentation/docs/dev-docker/README.md>`__.
-
-Reproducing the Docker image
----------------------------
-
-To reproduce this ROCm-enabled vLLM Docker image release, follow these steps:
-
-1. Clone the `vLLM repository <https://github.com/vllm-project/vllm>`__.
-
-   .. code-block:: shell
-
-      git clone https://github.com/vllm-project/vllm.git
-      cd vllm
-
-2. Use the following command to build the image directly from the specified commit.
-
-   .. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/vllm-benchmark-models.yaml
-
-      {% set docker = data.dockers[0] %}
-      .. code-block:: shell
-
-         docker build -f docker/Dockerfile.rocm \
-             --build-arg REMOTE_VLLM=1 \
-             --build-arg VLLM_REPO=https://github.com/ROCm/vllm \
-             --build-arg VLLM_BRANCH="{{ docker.dockerfile.commit }}" \
-             -t vllm-rocm .
-
-   .. tip::
-
-      Replace ``vllm-rocm`` with your desired image tag.
+After pulling the Docker image, follow the vLLM usage documentation: `Using
+vLLM <https://docs.vllm.ai/en/latest/usage/>`__.

 Further reading
 ===============

- To learn more about the options for latency and throughput benchmark scripts,
-  see `<https://github.com/ROCm/vllm/tree/main/benchmarks>`_.
-
- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
-
- To learn more about system settings and management practices to configure your system for
-  AMD Instinct MI300X Series GPUs, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
-
 - See :ref:`fine-tuning-llms-vllm` and :ref:`mi300x-vllm-optimization` for
  a brief introduction to vLLM and optimization strategies.

- For application performance optimization strategies for HPC and AI workloads,
-  including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
-
 - For a list of other ready-made Docker images for AI with ROCm, see
-  `AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
+  `AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`__.
+
+.. _vllm-inference-previous-versions:

 Previous versions
 =================

-See :doc:`previous-versions/vllm-history` to find documentation for previous releases
-of the ``ROCm/vllm`` Docker image.
+It's now recommended to use the upstream vLLM documentation at `docs.vllm.ai
+<https://docs.vllm.ai>`__ for the latest deployment guides.
+
+You can find legacy versions of this documentation at
+:doc:`previous-versions/vllm-history` which provide instructions for
+inference performance testing for select models. See the `Use AMD's Docker
+images
+<https://docs.vllm.ai/en/stable/deployment/docker/#use-amds-docker-images>`__
+note in the vLLM documentation for more information.
@@ -22,7 +22,7 @@ See the `GitHub repository <https://github.com/vllm-project/vllm>`_ and `officia
 <https://docs.vllm.ai/>`_ for more information.

 For guidance on using vLLM with ROCm, refer to `Installation with ROCm
-<https://docs.vllm.ai/en/latest/getting_started/amd-installation.html>`_.
+<https://docs.vllm.ai/en/stable/getting_started/installation/gpu.html#amd-rocm>`__.

 vLLM installation
 -----------------
@@ -5,11 +5,11 @@
 ****************************
 Use ROCm for AI inference
 ****************************
-AI inference is a process of deploying a trained machine learning model to make predictions or classifications on new data. This commonly involves using the model with real-time data and making quick decisions based on the predictions made by the model.  
+AI inference is a process of deploying a trained machine learning model to make predictions or classifications on new data. This commonly involves using the model with real-time data and making quick decisions based on the predictions made by the model. 

 Understanding the ROCm™ software platform’s architecture and capabilities is vital for running AI inference. By leveraging the ROCm platform's capabilities, you can harness the power of high-performance computing and efficient resource management to run inference workloads, leading to faster predictions and classifications on real-time data.

-Throughout the following topics, this section provides a comprehensive guide to setting up and deploying AI inference on AMD GPUs. This includes instructions on how to install ROCm, how to use Hugging Face Transformers to manage pre-trained models for natural language processing (NLP) tasks, how to validate vLLM on AMD Instinct™ MI300X GPUs and illustrate how to deploy trained models in production environments. 
+Throughout the following topics, this section provides a comprehensive guide to setting up and deploying AI inference on AMD GPUs. This includes instructions on how to install ROCm, how to use Hugging Face Transformers to manage pre-trained models for natural language processing (NLP) tasks, how to validate vLLM on AMD Instinct™ MI300X GPUs and illustrate how to deploy trained models in production environments.

 The AI Developer Hub contains `AMD ROCm tutorials <https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/>`_ for
 training, fine-tuning, and inference. It leverages popular machine learning frameworks on AMD GPUs.
@@ -20,10 +20,18 @@ training, fine-tuning, and inference. It leverages popular machine learning fram

 - :doc:`LLM inference frameworks <llm-inference-frameworks>`

- :doc:`vLLM inference performance testing <benchmark-docker/vllm>`
+- :doc:`vLLM inference <benchmark-docker/vllm>`

 - :doc:`PyTorch inference performance testing <benchmark-docker/pytorch-inference>`

 - :doc:`SGLang inference performance testing <benchmark-docker/sglang>`

+- :doc:`vLLM distributed inference with MoRI <benchmark-docker/vllm-mori-distributed>`
+
+- :doc:`SGLang distributed inference with MoRI <benchmark-docker/sglang-mori-distributed>`
+
+- :doc:`SGLang distributed inference with Mooncake <benchmark-docker/sglang-distributed>`
+
+- :doc:`xDiT diffusion inference <xdit-diffusion-inference>`
+
 - :doc:`Deploying your model <deploy-your-model>`
@@ -0,0 +1,310 @@
+.. meta::
+   :description: Learn to validate diffusion model video generation on MI300X, MI350X and MI355X accelerators using
+                 prebuilt and optimized docker images.
+   :keywords: xDiT, diffusion, video, video generation, image, image generation, validate, benchmark
+
+************************
+xDiT diffusion inference
+************************
+
+.. _xdit-video-diffusion:
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/xdit-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   The `rocm/pytorch-xdit <{{ docker.docker_hub_url }}>`_ Docker image offers a prebuilt, optimized environment based on `xDiT <https://github.com/xdit-project/xDiT>`_ for
+   benchmarking diffusion model video and image generation on gfx942 and gfx950 series (AMD Instinct™ MI300X, MI325X, MI350X, and MI355X) GPUs.
+   The image runs ROCm **{{docker.ROCm}}** (preview) based on `TheRock <https://github.com/ROCm/TheRock>`_
+   and includes the following components:
+
+   .. dropdown:: Software components - {{ docker.pull_tag.split('-')|last }}
+
+      .. list-table::
+         :header-rows: 1
+
+         * - Software component
+           - Version
+
+         {% for component_name, component_data in docker.components.items() %}
+         * - `{{ component_name }} <{{ component_data.url }}>`_
+           - {{ component_data.version }}
+         {% endfor %}
+
+Follow this guide to pull the required image, spin up a container, download the model, and run a benchmark.
+For preview and development releases, see `amdsiloai/pytorch-xdit <https://hub.docker.com/r/amdsiloai/pytorch-xdit>`_.
+
+What's new
+==========
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/xdit-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   {% for item in docker.whats_new %}
+   * {{ item }}
+   {% endfor %}
+
+.. _xdit-video-diffusion-supported-models:
+
+Supported models
+================
+
+The following models are supported for inference performance benchmarking.
+Some instructions, commands, and recommendations in this documentation might
+vary by model -- select one to get started.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/xdit-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   .. raw:: html
+
+      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
+          <div class="row gx-0">
+              <div class="col-2 me-1 px-2 model-param-head">Model</div>
+              <div class="row col-10 pe-0">
+        {% for model_group in docker.supported_models %}
+               <div class="col-6 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.js_tag }}" tabindex="0">{{ model_group.group }}</div>
+        {% endfor %}
+              </div>
+          </div>
+
+          <div class="row gx-0 pt-1">
+              <div class="col-2 me-1 px-2 model-param-head">Variant</div>
+              <div class="row col-10 pe-0">
+        {% for model_group in docker.supported_models %}
+            {% set models = model_group.models %}
+            {% for model in models %}
+                {% if models|length % 3 == 0 %}
+                <div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.js_tag }}" data-param-group="{{ model_group.js_tag }}" tabindex="0">{{ model.model }}</div>
+                {% else %}
+                <div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.js_tag }}" data-param-group="{{ model_group.js_tag }}" tabindex="0">{{ model.model }}</div>
+                {% endif %}
+            {% endfor %}
+        {% endfor %}
+              </div>
+          </div>
+      </div>
+
+   {% for model_group in docker.supported_models %}
+       {% for model in model_group.models %}
+
+   .. container:: model-doc {{ model.js_tag }}
+
+      .. note::
+
+         To learn more about your specific model see the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`_
+         or visit the `GitHub page <{{ model.github }}>`__. Note that some models require access authorization before use via an
+         external license agreement through a third party.
+
+       {% endfor %}
+   {% endfor %}
+
+System validation
+=================
+
+Before running AI workloads, it's important to validate that your AMD hardware is configured
+correctly and performing optimally.
+
+If you have already validated your system settings, including aspects like NUMA auto-balancing, you
+can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
+optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
+before starting.
+
+To test for optimal performance, consult the recommended :ref:`System health benchmarks
+<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
+system's configuration.
+
+Pull the Docker image
+=====================
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/xdit-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   For this tutorial, it's recommended to use the latest ``{{ docker.pull_tag }}`` Docker image.
+   Pull the image using the following command:
+
+   .. code-block:: shell
+
+      docker pull {{ docker.pull_tag }}
+
+Validate and benchmark
+======================
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/xdit-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   Once the image has been downloaded you can follow these steps to
+   run benchmarks and generate outputs.
+
+   {% for model_group in docker.supported_models %}
+     {% for model in model_group.models %}
+
+   .. container:: model-doc {{model.js_tag}}
+
+      The following commands are written for {{ model.model }}.
+      See :ref:`xdit-video-diffusion-supported-models` to switch to another available model.
+
+     {% endfor %}
+   {% endfor %}
+
+Choose your setup method
+------------------------
+
+You can either use an existing Hugging Face cache or download the model fresh inside the container.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/xdit-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   {% for model_group in docker.supported_models %}
+     {% for model in model_group.models %}
+   .. container:: model-doc {{model.js_tag}}
+
+      .. tab-set::
+
+         .. tab-item:: Option 1: Use existing Hugging Face cache
+
+            If you already have models downloaded on your host system, you can mount your existing cache.
+
+            1. Set your Hugging Face cache location.
+
+               .. code-block:: shell
+
+                  export HF_HOME=/your/hf_cache/location
+
+            2. Download the model (if not already cached).
+
+               .. code-block:: shell
+
+                  huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
+
+            3. Launch the container with mounted cache.
+
+               .. code-block:: shell
+
+                  docker run \
+                      -it --rm \
+                      --cap-add=SYS_PTRACE \
+                      --security-opt seccomp=unconfined \
+                      --user root \
+                      --device=/dev/kfd \
+                      --device=/dev/dri \
+                      --group-add video \
+                      --ipc=host \
+                      --network host \
+                      --privileged \
+                      --shm-size 128G \
+                      --name pytorch-xdit \
+                      -e HSA_NO_SCRATCH_RECLAIM=1 \
+                      -e OMP_NUM_THREADS=16 \
+                      -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+                      -e HF_HOME=/app/huggingface_models \
+                      -v $HF_HOME:/app/huggingface_models \
+                      {{ docker.pull_tag }}
+
+         .. tab-item:: Option 2: Download inside container
+
+            If you prefer to keep the container self-contained or don't have an existing cache.
+
+            1. Launch the container
+
+               .. code-block:: shell
+
+                  docker run \
+                      -it --rm \
+                      --cap-add=SYS_PTRACE \
+                      --security-opt seccomp=unconfined \
+                      --user root \
+                      --device=/dev/kfd \
+                      --device=/dev/dri \
+                      --group-add video \
+                      --ipc=host \
+                      --network host \
+                      --privileged \
+                      --shm-size 128G \
+                      --name pytorch-xdit \
+                      -e HSA_NO_SCRATCH_RECLAIM=1 \
+                      -e OMP_NUM_THREADS=16 \
+                      -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+                      {{ docker.pull_tag }}
+
+            2. Inside the container, set the Hugging Face cache location and download the model.
+
+               .. code-block:: shell
+
+                  export HF_HOME=/app/huggingface_models
+                  huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
+
+               .. warning::
+
+                  Models will be downloaded to the container's filesystem and will be lost when the container is removed unless you persist the data with a volume.
+     {% endfor %}
+   {% endfor %}
+
+Run inference
+=============
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/xdit-inference-models.yaml
+
+   {% set docker = data.docker %}
+
+   {% for model_group in docker.supported_models %}
+     {% for model in model_group.models %}
+
+   .. container:: model-doc {{ model.js_tag }}
+
+      .. tab-set::
+
+         .. tab-item:: MAD-integrated benchmarking
+
+            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
+               directory and install the required packages on the host machine.
+
+               .. code-block:: shell
+
+                  git clone https://github.com/ROCm/MAD
+                  cd MAD
+                  pip install -r requirements.txt
+
+            2. On the host machine, use this command to run the performance benchmark test on
+               the `{{model.model}} <{{ model.url }}>`_ model using one node.
+
+               .. code-block:: shell
+
+                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
+                  madengine run \
+                      --tags {{model.mad_tag}} \
+                      --keep-model-dir \
+                      --live-output
+
+            MAD launches a Docker container with the name
+            ``container_ci-{{model.mad_tag}}``. The throughput and serving reports of the
+            model are collected in the following paths: ``{{ model.mad_tag }}_throughput.csv``
+            and ``{{ model.mad_tag }}_serving.csv``.
+
+         .. tab-item:: Standalone benchmarking
+
+            To run the benchmarks for {{ model.model }}, use the following command:
+
+            .. code-block:: shell
+
+               {{ model.benchmark_command
+                  | map('replace', '{model_repo}', model.model_repo)
+                  | map('trim')
+                  | join('\n               ') }}
+
+            The generated content and timing information will be stored under the results directory.
+
+      {% endfor %}
+    {% endfor %}
+
+Previous versions
+=================
+
+See
+:doc:`/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-history`
+to find documentation for previous releases of xDiT diffusion inference
+performance testing.
@@ -254,7 +254,7 @@ PyTorch training
   The ROCm PyTorch Training Docker image now focuses on :doc:`Training a model
   with Primus and PyTorch <../training/benchmark-docker/primus-pytorch>`. The
   following example refers to the legacy workflow :ref:`Training a
-   model with PyTorch <amd-pytorch-training-multinode-examples>`.
+   model with PyTorch <amd-pytorch-training-multinode-examples-v259>`.

 1. Download the ``run_multinode_train.sh`` benchmarking script from `<https://github.com/ROCm/MAD/tree/develop/scripts/pytorch_train>`__.

@@ -277,7 +277,7 @@ PyTorch training

 .. seealso::

-   See :ref:`Training a model with PyTorch <amd-pytorch-multinode-examples>` for more examples and information.
+   See :ref:`Training a model with PyTorch <amd-pytorch-training-multinode-examples-v259>` for more examples and information.

 Megatron-LM
 -----------
@@ -31,16 +31,16 @@ in the Instinct documentation for more information.
 Hardware verification with ROCm
 -------------------------------

-Use the command ``rocm-smi --setperfdeterminism 1900`` to set the max clock speed up to 1900 MHz
+Use the command ``amd-smi set --perf-determinism 1900`` to set the max clock speed up to 1900 MHz
 instead of the default 2100 MHz. This can reduce the chance of a PCC event lowering the attainable
 GPU clocks. This setting will not be required for new IFWI releases with the production PRC feature.
-You can restore this setting to its default value with the ``rocm-smi -r`` command.
+You can restore this setting to its default value with the ``amd-smi reset --clocks`` command.

 Run the command:

 .. code-block:: shell

-   rocm-smi --setperfdeterminism 1900
+   amd-smi set --perf-determinism 1900

 See `Hardware verfication for ROCm <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html#hardware-verification-with-rocm>`_
 in the Instinct documentation for more information.
@@ -92,7 +92,7 @@ GPUs, which can impact end-to-end latency.
 .. _healthcheck-install-transferbench:

 1. To get started, use the instructions in the `TransferBench documentation
-   <https://rocm.docs.amd.com/projects/TransferBench/en/latest/install/install.html#install-transferbench>`_
+   <https://rocm.docs.amd.com/projects/TransferBench/en/latest/install/install.html#install-transferbench>`__
   or use the following commands:

   .. code:: shell
@@ -102,5 +102,5 @@ GPUs, which can impact end-to-end latency.
      CC=hipcc make

 2. Run the suggested TransferBench tests -- see `TransferBench benchmarking
-   <https://instinct.docs.amd.com/projects/system-acceptance/en/latest/mi300x/performance-bench.html#transferbench-benchmarking-results>`_
+   <https://instinct.docs.amd.com/projects/system-acceptance/en/latest/common/system-validation.html#transferbench>`__
   in the Instinct performance benchmarking documentation for instructions.
@@ -2,13 +2,18 @@
   :description: How to train a model using JAX MaxText for ROCm.
   :keywords: ROCm, AI, LLM, train, jax, torch, Llama, flux, tutorial, docker

-******************************************
-Training a model with JAX MaxText on ROCm
-******************************************
+********************************************
+Training a model with Primus and JAX MaxText
+********************************************
+
+The JAX MaxText for ROCm training Docker image provides a prebuilt environment
+for training on AMD Instinct MI355X, MI350X, MI325X, and MI300X GPUs, with
+essential components such as JAX, XLA, ROCm libraries, and MaxText utilities.
+
+The image also integrates with `Primus <https://github.com/AMD-AGI/Primus>`__,
+a high-level training framework that supports multiple backends. You can use
+the unified ``primus-cli`` to run training jobs using the JAX MaxText backend.

-The MaxText for ROCm training Docker image
-provides a prebuilt environment for training on AMD Instinct MI355X, MI350X, MI325X, and MI300X GPUs,
-including essential components like JAX, XLA, ROCm libraries, and MaxText utilities.
 It includes the following software components:

 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/jax-maxtext-benchmark-models.yaml
@@ -33,16 +38,6 @@ It includes the following software components:
              - {{ component_version }}

            {% endfor %}
-         {% if jax_version == "0.6.0" %}
-         .. note::
-
-            Shardy is a new config in JAX 0.6.0. You might get related errors if it's
-            not configured correctly. For now you can turn it off by setting
-            ``shardy=False`` during the training run. You can also follow the `migration
-            guide <https://docs.jax.dev/en/latest/shardy_jax_migration.html>`__ to enable
-            it.
-         {% endif %}
-
      {% endfor %}

 MaxText with on ROCm provides the following key features to train large language models efficiently:
@@ -57,7 +52,7 @@ MaxText with on ROCm provides the following key features to train large language

 - NANOO FP8 (for MI300X series GPUs) and FP8 (for MI355X and MI350X) quantization support

-.. _amd-maxtext-model-support-v259:
+.. _amd-maxtext-model-support-v26.2:

 Supported models
 ================
@@ -139,7 +134,7 @@ Use the following command to pull the Docker image from Docker Hub.

      docker pull {{ docker.pull_tag }}

-.. _amd-maxtext-multi-node-setup-v259:
+.. _amd-maxtext-multi-node-setup-v26.2:

 Multi-node configuration
 ------------------------
@@ -147,7 +142,7 @@ Multi-node configuration
 See :doc:`/how-to/rocm-for-ai/system-setup/multi-node-setup` to configure your
 environment for multi-node training.

-.. _amd-maxtext-get-started-v259:
+.. _amd-maxtext-get-started-v26.2:

 Benchmarking
 ============
@@ -168,11 +163,145 @@ benchmark results:

      .. tab-set::

+         {% if model.primus_config_name %}
+         .. tab-item:: Primus benchmarking
+
+            .. container:: model-doc {{ model.mad_tag }}
+
+               The following run commands are tailored to {{ model.model }}.
+               See :ref:`amd-maxtext-model-support-v26.2` to switch to another available model.
+
+               .. rubric:: Download the Docker image and required packages
+
+               1. Pull the ``{{ docker.pull_tag }}`` Docker image from Docker Hub.
+
+                  .. code-block:: shell
+
+                     docker pull {{ docker.pull_tag }}
+
+               2. Run the Docker container.
+
+                  .. code-block:: shell
+
+                     docker run -it \
+                         --device /dev/dri \
+                         --device /dev/kfd \
+                         --network host \
+                         --ipc host \
+                         --group-add video \
+                         --cap-add SYS_PTRACE \
+                         --security-opt seccomp=unconfined \
+                         --privileged \
+                         -v $HOME:$HOME \
+                         -v $HOME/.ssh:/root/.ssh \
+                         -v $HF_HOME:/hf_cache \
+                         -e HF_HOME=/hf_cache \
+                         -e MAD_SECRETS_HFTOKEN=$MAD_SECRETS_HFTOKEN
+                         --shm-size 64G \
+                         --name training_env \
+                         {{ docker.pull_tag }}
+
+                  Use these commands if you exit the ``training_env`` container and need to return to it.
+
+                  .. code-block:: shell
+
+                     docker start training_env
+                     docker exec -it training_env bash
+
+               3. Clone the Primus repository.
+
+                  .. code-block:: shell
+
+                     git clone https://github.com/AMD-AIG-AIMA/Primus.git
+                     cd Primus
+                     git checkout dev/fuyuajin/maxtext-backend-test
+                     git submodule update --init third_party/maxtext/
+
+               .. rubric:: Run the training job with primus-cli
+
+               For detailed usage instructions for ``primus-cli``, see the
+               `Primus CLI User Guide
+               <https://github.com/AMD-AGI/Primus/blob/main/docs/cli/PRIMUS-CLI-GUIDE.md>`__.
+
+               Use the following examples to run training with ``primus-cli``:
+
+               - Direct mode: run directly on the current host or within an existing Docker container
+
+                 .. tab-set::
+
+                    .. tab-item:: MI355X
+                       :sync: mi355x
+
+                       .. code-block:: shell
+
+                          ./primus-cli direct \
+                            -- train pretrain \
+                            --config examples/maxtext/configs/MI355X/{{ model.primus_config_name }}
+
+                    .. tab-item:: MI300X
+                       :sync: mi300x
+
+                       .. code-block:: shell
+
+                          ./primus-cli direct \
+                            -- train pretrain \
+                            --config examples/maxtext/configs/MI300X/{{ model.primus_config_name }}
+
+               - Container mode: run in Docker containers
+
+                 .. tab-set::
+
+                    .. tab-item:: MI355X
+                       :sync: mi355x
+
+                       .. code-block:: shell
+
+                          ./primus-cli container --image {{ docker.pull_tag }} \
+                            -- train pretrain \
+                            --config examples/maxtext/configs/MI355X/{{ model.primus_config_name }}
+
+                    .. tab-item:: MI300X
+                       :sync: mi300x
+
+                       .. code-block:: shell
+
+                          ./primus-cli container --image rocm/jax-training:maxtext-v26.2 \
+                            -- train pretrain \
+                            --config examples/maxtext/configs/MI300X/{{ model.primus_config_name }}
+
+
+               - Slurm mode: run distributed training on a Slurm cluster
+
+                 .. tab-set::
+
+                    .. tab-item:: MI355X
+                       :sync: mi355x
+
+                       .. code-block:: shell
+
+                          # Use a custom config file, where you can specify
+                          # the Docker image and set environment variables.
+                          ./primus-cli --config my_maxtext_config.yaml slurm srun -N 8 \
+                            -- train pretrain \
+                            --config examples/maxtext/configs/MI355X/{{ model.primus_config_name }}
+
+                    .. tab-item:: MI300X
+                       :sync: mi300x
+
+                       .. code-block:: shell
+
+                          # Use a custom config file, where you can specify
+                          # the Docker image and set environment variables.
+                          ./primus-cli --config my_maxtext_config.yaml slurm srun -N 8 \
+                            -- train pretrain \
+                            --config examples/maxtext/configs/MI300X/{{ model.primus_config_name }}
+         {% endif %}
+
         {% if model.mad_tag and "single-node" in model.doc_options %}
         .. tab-item:: MAD-integrated benchmarking

            The following run command is tailored to {{ model.model }}.
-            See :ref:`amd-maxtext-model-support-v259` to switch to another available model.
+            See :ref:`amd-maxtext-model-support-v26.2` to switch to another available model.

            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
               directory and install the required packages on the host machine.
@@ -203,7 +332,7 @@ benchmark results:
         .. tab-item:: Standalone benchmarking

            The following commands are optimized for {{ model.model }}. See
-            :ref:`amd-maxtext-model-support-v259` to switch to another
+            :ref:`amd-maxtext-model-support-v26.2` to switch to another
            available model. Some instructions and resources might not be
            available for all models and configurations.

@@ -299,41 +428,68 @@ benchmark results:
                  {% endif %}

            {% endif %}
-            {% if model.multinode_training_script and "multi-node" in model.doc_options %}
+            {% if model.multinode_config and "multi-node" in model.doc_options %}
            .. rubric:: Multi-node training

-            The following examples use SLURM to run on multiple nodes.
+            The following SLURM scripts will launch the Docker container and
+            run the benchmark. Run them outside of any Docker container. The
+            unified multi-node benchmark script accepts a configuration file
+            that specifies the model and training parameters.

-            .. note::
+            .. code-block:: shell

-               The following scripts will launch the Docker container and run the
-               benchmark. Run them outside of any Docker container.
+               sbatch -N <NUM_NODES> jax_maxtext_multinode_benchmark.sh <config_file.yml> [docker_image]

-            1. Make sure ``$HF_HOME`` is set before running the test. See
-               `ROCm benchmarking <https://github.com/ROCm/MAD/blob/develop/scripts/jax-maxtext/gpu-rocm/readme.md>`__
-               for more details on downloading the Llama models before running the
-               benchmark.
+            <NUM_NODES>
+               The number of nodes to use for training (for example, 2, 4,
+               8).

-            2. To run multi-node training for {{ model.model }},
-               use the
-               `multi-node training script <https://github.com/ROCm/MAD/blob/develop/scripts/jax-maxtext/gpu-rocm/{{ model.multinode_training_script }}>`__
-               under the ``scripts/jax-maxtext/gpu-rocm/`` directory.
+            <config_file.yml>
+               Path to the YAML configuration file containing model and
+               training parameters. Configuration files are available in the
+               ``scripts/jax-maxtext/env_scripts/`` directory for different
+               models and GPU architectures.

-            3. Run the multi-node training benchmark script.
+            [docker_image] (optional)
+               The Docker image to use. If not specified, it defaults to
+               ``rocm/jax-training:maxtext-v26.2``.

-               .. code-block:: shell
+            For example, to run a multi-node training benchmark on {{ model.model }}:

-                  sbatch -N <num_nodes> {{ model.multinode_training_script }}
+            .. tab-set::
+
+               {% if model.multinode_config.gfx950 %}
+               .. tab-item:: MI355X and MI350X (gfx950)
+
+                  .. code-block:: bash
+
+                     sbatch -N 4 jax_maxtext_multinode_benchmark.sh {{ model.multinode_config.gfx950 }}
+               {% endif %}
+
+               {% if model.multinode_config.gfx942 %}
+               .. tab-item:: MI325X and MI300X (gfx942)
+
+                  .. code-block:: bash
+
+                     sbatch -N 4 jax_maxtext_multinode_benchmark.sh {{ model.multinode_config.gfx942 }}
+               {% endif %}

         {% else %}
            .. rubric:: Multi-node training

-            For multi-node training examples, choose a model from :ref:`amd-maxtext-model-support-v259`
-            with an available `multi-node training script <https://github.com/ROCm/MAD/tree/develop/scripts/jax-maxtext/gpu-rocm>`__.
+            For multi-node training examples, choose a model from :ref:`amd-maxtext-model-support-v26.2`
+            with an available `multi-node training script <https://github.com/ROCm/MAD/tree/develop/scripts/jax-maxtext/env_scripts>`__.
         {% endif %}
      {% endfor %}
   {% endfor %}

+Known issues
+============
+
+- You might see NaNs in the losses when setting ``packing=True``. As
+  a workaround, turn off input sequence packing (``packing=False``).
+  This will be fixed in a future release.
+
 Further reading
 ===============

@@ -14,7 +14,7 @@ Training a model with Megatron-LM on ROCm
   <https://hub.docker.com/r/rocm/megatron-lm/>`__ Docker Hub registry will be
   deprecated soon in favor of `rocm/primus <https://hub.docker.com/r/rocm/primus>`__.
   The ``rocm/primus`` Docker containers will cover PyTorch training ecosystem frameworks,
-   including Megatron-LM, `torchtitan, and torchtune <primus-pytorch>`__.
+   including Megatron-LM and :doc:`torchtitan <primus-pytorch>`.

   Primus with Megatron is designed to replace this ROCm Megatron-LM training workflow.
   To learn how to migrate workloads from Megatron-LM to Primus with Megatron,
@@ -36,12 +36,10 @@ accelerate training workloads:

 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/megatron-lm-benchmark-models.yaml

-   {% set dockers = data.dockers %}
   .. tab-set::

-   {% for supported_gpus, docker in dockers.items() %}
-      .. tab-item:: {{ supported_gpus }}
-         :sync: {{ supported_gpus }}
+      .. tab-item:: {{ data.docker.pull_tag }}
+         :sync: {{ data.docker.pull_tag }}

         .. list-table::
            :header-rows: 1
@@ -49,12 +47,12 @@ accelerate training workloads:
            * - Software component
              - Version

-            {% for component_name, component_version in docker.components.items() %}
+            {% for component_name, component_version in data.docker.components.items() %}
            * - {{ component_name }}
              - {{ component_version }}
            {% endfor %}
-   {% endfor %}
-   .. _amd-megatron-lm-model-support:
+
+   .. _amd-megatron-lm-model-support-v26.01:

   Supported models
   ================
@@ -99,7 +97,7 @@ accelerate training workloads:
   Some models, such as Llama, require an external license agreement through
   a third party (for example, Meta).

-.. _amd-megatron-lm-performance-measurements:
+.. _amd-megatron-lm-performance-measurements-v26.01:

 Performance measurements
 ========================
@@ -131,7 +129,7 @@ To test for optimal performance, consult the recommended :ref:`System health ben
 <rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
 system's configuration.

-.. _mi300x-amd-megatron-lm-training:
+.. _mi300x-amd-megatron-lm-training-v26.01:

 Environment setup
 =================
@@ -140,52 +138,38 @@ Use the following instructions to set up the environment, configure the script t
 reproduce the benchmark results on MI300X Series GPUs with the AMD Megatron-LM Docker
 image.

-.. _amd-megatron-lm-requirements:
+.. _amd-megatron-lm-requirements-v26.01:

 Download the Docker image
 -------------------------

 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/megatron-lm-benchmark-models.yaml

-   {% set dockers = data.dockers %}
+   {% set docker = data.docker %}
   1. Use the following command to pull the Docker image from Docker Hub.

-      .. tab-set::
+      .. code-block:: shell

-         {% for supported_gpus, docker in dockers.items() %}
-         .. tab-item:: {{ supported_gpus }}
-            :sync: {{ supported_gpus }}
-
-            .. code-block:: shell
-
-               docker pull {{ docker.pull_tag }}
-         {% endfor %}
+         docker pull {{ docker.pull_tag }}

   2. Launch the Docker container.

-      .. tab-set::
+      .. code-block:: shell

-         {% for supported_gpus, docker in dockers.items() %}
-         .. tab-item:: {{ supported_gpus }}
-            :sync: {{ supported_gpus }}
-
-            .. code-block:: shell
-
-               docker run -it \
-                   --device /dev/dri \
-                   --device /dev/kfd \
-                   --device /dev/infiniband \
-                   --network host --ipc host \
-                   --group-add video \
-                   --cap-add SYS_PTRACE \
-                   --security-opt seccomp=unconfined \
-                   --privileged \
-                   -v $HOME:$HOME \
-                   -v $HOME/.ssh:/root/.ssh \
-                   --shm-size 128G \
-                   --name megatron_training_env \
-                   {{ docker.pull_tag }}
-         {% endfor %}
+         docker run -it \
+             --device /dev/dri \
+             --device /dev/kfd \
+             --device /dev/infiniband \
+             --network host --ipc host \
+             --group-add video \
+             --cap-add SYS_PTRACE \
+             --security-opt seccomp=unconfined \
+             --privileged \
+             -v $HOME:$HOME \
+             -v $HOME/.ssh:/root/.ssh \
+             --shm-size 128G \
+             --name megatron_training_env \
+             {{ docker.pull_tag }}

 3. Use these commands if you exit the ``megatron_training_env`` container and need to return to it.

@@ -206,7 +190,7 @@ Download the Docker image
 The Docker container hosts a verified commit of
 `<https://github.com/ROCm/Megatron-LM/tree/rocm_dev>`__.

-.. _amd-megatron-lm-environment-setup:
+.. _amd-megatron-lm-environment-setup-v26.01:

 Configuration
 =============
@@ -216,39 +200,39 @@ Configuration
   Update the ``train_llama3.sh`` configuration script in the ``examples/llama``
   directory of
   `<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/llama>`__ to configure your training run.
-   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training>`.
+   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v26.01>`.

 .. container:: model-doc pyt_megatron_lm_train_llama-2-7b pyt_megatron_lm_train_llama-2-70b

   Update the ``train_llama2.sh`` configuration script in the ``examples/llama``
   directory of
   `<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/llama>`__ to configure your training run.
-   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training>`.
+   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v26.01>`.

 .. container:: model-doc pyt_megatron_lm_train_deepseek-v3-proxy

   Update the ``train_deepseekv3.sh`` configuration script in the ``examples/deepseek_v3``
   directory of
   `<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/deepseek_v3>`__ to configure your training run.
-   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training>`.
+   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v26.01>`.

 .. container:: model-doc pyt_megatron_lm_train_deepseek-v2-lite-16b

   Update the ``train_deepseekv2.sh`` configuration script in the ``examples/deepseek_v2``
   directory of
   `<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/deepseek_v2>`__ to configure your training run.
-   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training>`.
+   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v26.01>`.

 .. container:: model-doc pyt_megatron_lm_train_mixtral-8x7b pyt_megatron_lm_train_mixtral-8x22b-proxy

   Update the ``train_mixtral_moe.sh`` configuration script in the ``examples/mixtral``
   directory of
   `<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/mixtral>`__ to configure your training run.
-   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training>`.
+   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v26.01>`.

 .. note::

-   See :ref:`Key options <amd-megatron-lm-benchmark-test-vars>` for more information on configuration options.
+   See :ref:`Key options <amd-megatron-lm-benchmark-test-vars-v26.01>` for more information on configuration options.

 Multi-node configuration
 ------------------------
@@ -256,7 +240,7 @@ Multi-node configuration
 Refer to :doc:`/how-to/rocm-for-ai/system-setup/multi-node-setup` to configure your environment for multi-node
 training. See :ref:`amd-megatron-lm-multi-node-examples` for example run commands.

-.. _amd-megatron-lm-tokenizer:
+.. _amd-megatron-lm-tokenizer-v26.01:

 Tokenizer
 ---------
@@ -393,7 +377,7 @@ Download the dataset

   ``TOKENIZER_MODEL`` can be any accessible Hugging Face tokenizer.
   Remember to either pre-download the tokenizer or setup Hugging Face access
-   otherwise when needed -- see the :ref:`Tokenizer <amd-megatron-lm-tokenizer>` section.
+   otherwise when needed -- see the :ref:`Tokenizer <amd-megatron-lm-tokenizer-v26.01>` section.

   .. note::

@@ -495,15 +479,38 @@ Download the dataset

   Ensure that the files are accessible inside the Docker container.

-.. _amd-megatron-lm-run-training:
+.. _amd-megatron-lm-run-training-v26.01:

 Run training
 ============

 Use the following example commands to set up the environment, configure
-:ref:`key options <amd-megatron-lm-benchmark-test-vars>`, and run training on
+:ref:`key options <amd-megatron-lm-benchmark-test-vars-v26.01>`, and run training on
 MI300X Series GPUs with the AMD Megatron-LM environment.

+Before starting training, export the following environment variables.
+
+.. tab-set::
+
+   .. tab-item:: MI355X and MI350X
+
+      .. code-block:: shell
+
+         export HSA_NO_SCRATCH_RECLAIM=1
+         export NVTE_CK_USES_BWD_V3=1
+         export NVTE_CK_USES_BWD_V3=1
+
+   .. tab-item:: MI325X and MI300X
+
+      .. code-block:: shell
+
+         export HSA_NO_SCRATCH_RECLAIM=1
+         export NVTE_CK_USES_BWD_V3=1
+         export NVTE_CK_USES_BWD_V3=1
+
+         # Set this on MI325X/MI300X only
+         export NVTE_CK_IS_V3_ATOMIC_FP32=1
+
 Single node training
 --------------------

@@ -913,7 +920,7 @@ Single node training
          RECOMPUTE_ACTIVATIONS=full \
          CKPT_FORMAT=torch_dist

-.. _amd-megatron-lm-multi-node-examples:
+.. _amd-megatron-lm-multi-node-examples-v26.01:

 Multi-node training examples
 ----------------------------
@@ -964,7 +971,7 @@ training on 16 nodes, try the following command:

   sbatch examples/deepseek_v3/train_deepseek_v3_slurm.sh

-.. _amd-megatron-lm-benchmark-test-vars:
+.. _amd-megatron-lm-benchmark-test-vars-v26.01:

 Key options
 -----------
@@ -1029,11 +1036,6 @@ The benchmark tests support the following sets of variables.
 ``RECOMPUTE_NUM_LAYERS``
  Number of layers used for checkpointing recompute.

-Known issues
-============
-
-PyTorch Profiler may produce inaccurate traces when CPU activity profiling is enabled.
-
 Previous versions
 =================

@@ -17,13 +17,38 @@ previous releases of the ``ROCm/jax-training`` Docker image on `Docker Hub <http
     - Components
     - Resources

-   * - 25.9 (latest)
+   * - 26.2 (latest)
+     -
+       * ROCm 7.1.1
+       * JAX 0.8.2
+     -
+       * :doc:`Documentation <../jax-maxtext>`
+       * `Docker Hub <https://hub.docker.com/layers/rocm/jax-training/maxtext-v26.2/images/sha256-a89643388487b1e2fc6b6ef7bd3c44378c05d217309c977a1c18c72d05ebcaeb>`__
+
+   * - 26.1
+     -
+       * ROCm 7.1.1
+       * JAX 0.8.2
+     -
+       * :doc:`Documentation <jax-maxtext-v26.1>`
+       * `Docker Hub <https://hub.docker.com/layers/rocm/jax-training/maxtext-v26.1/images/sha256-901083bde353fe6362ada3036e452c792b2c96124e5900f4e9b5946c02ff9d6a>`__
+
+   * - 25.11
+     -
+       * ROCm 7.1.0
+       * JAX 0.7.1
+     -
+       * :doc:`Documentation <jax-maxtext-v25.11>`
+       * `Docker Hub <https://hub.docker.com/layers/rocm/jax-training/maxtext-v25.11/images/sha256-18e4d8f0b8ce7a7422c58046940dd5f32249960449fca09a562b65fb8eb1562a>`__
+
+   * - 25.9.1
     -
       * ROCm 7.0.0
       * JAX 0.6.2
     -
-       * :doc:`Documentation <../jax-maxtext>`
-       * `Docker Hub <https://hub.docker.com/layers/rocm/jax-training/maxtext-v25.7-jax060/images/sha256-7352212ae033a76dca2b9dceffc23c1b5f1a61a7a560082cf747a9bf1acfc9ce>`__
+       * :doc:`Documentation <jax-maxtext-v25.9>`
+       * `Docker Hub (25.9.1) <https://hub.docker.com/layers/rocm/jax-training/maxtext-v25.9.1/images/sha256-60946cfbd470f6ee361fc9da740233a4fb2e892727f01719145b1f7627a1cff6>`__
+       * `Docker Hub (25.9) <https://hub.docker.com/layers/rocm/jax-training/maxtext-v25.9/images/sha256-4bb16ab58279ef09cb7a5e362c38e3fe3f901de44d8dbac5d0cb3bac5686441e>`__

   * - 25.7
     -
@@ -0,0 +1,408 @@
+:orphan:
+
+.. meta::
+   :description: How to train a model using JAX MaxText for ROCm.
+   :keywords: ROCm, AI, LLM, train, jax, torch, Llama, flux, tutorial, docker
+
+******************************************
+Training a model with JAX MaxText on ROCm
+******************************************
+
+.. caution::
+
+   This documentation does not reflect the latest version of ROCm JAX MaxText
+   training performance documentation. See :doc:`../jax-maxtext` for the latest version.
+
+The MaxText for ROCm training Docker image
+provides a prebuilt environment for training on AMD Instinct MI355X, MI350X, MI325X, and MI300X GPUs,
+including essential components like JAX, XLA, ROCm libraries, and MaxText utilities.
+It includes the following software components:
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/jax-maxtext-v25.11-benchmark-models.yaml
+
+   {% set dockers = data.dockers %}
+   .. tab-set::
+
+      {% for docker in dockers %}
+      {% set jax_version = docker.components["JAX"] %}
+
+      .. tab-item:: ``{{ docker.pull_tag }}``
+         :sync: {{ docker.pull_tag }}
+
+         .. list-table::
+            :header-rows: 1
+
+            * - Software component
+              - Version
+
+            {% for component_name, component_version in docker.components.items() %}
+            * - {{ component_name }}
+              - {{ component_version }}
+
+            {% endfor %}
+      {% endfor %}
+
+.. note::
+
+   The ``rocm/jax-training:maxtext-v25.9`` has been updated to
+   ``rocm/jax-training:maxtext-v25.9.1``. This revision should include
+   a fix to address segmentation fault issues during launch. See the
+   :doc:`versioned documentation <jax-maxtext-v25.9>`.
+
+MaxText with on ROCm provides the following key features to train large language models efficiently:
+
+- Transformer Engine (TE)
+
+- Flash Attention (FA) 3 -- with or without sequence input packing
+
+- GEMM tuning
+
+- Multi-node support
+
+- NANOO FP8 (for MI300X series GPUs) and FP8 (for MI355X and MI350X) quantization support
+
+.. _amd-maxtext-model-support-v25.11:
+
+Supported models
+================
+
+The following models are pre-optimized for performance on AMD Instinct
+GPUs. Some instructions, commands, and available training
+configurations in this documentation might vary by model -- select one to get
+started.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/jax-maxtext-v25.11-benchmark-models.yaml
+
+   {% set model_groups = data.model_groups %}
+   .. raw:: html
+
+      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
+         <div class="row gx-0">
+            <div class="col-2 me-1 px-2 model-param-head">Model</div>
+            <div class="row col-10 pe-0">
+      {% for model_group in model_groups %}
+               <div class="col-4 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
+      {% endfor %}
+            </div>
+         </div>
+
+         <div class="row gx-0 pt-1">
+            <div class="col-2 me-1 px-2 model-param-head">Variant</div>
+            <div class="row col-10 pe-0">
+      {% for model_group in model_groups %}
+         {% set models = model_group.models %}
+         {% for model in models %}
+            {% if models|length % 3 == 0 %}
+               <div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
+            {% else %}
+               <div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
+            {% endif %}
+         {% endfor %}
+      {% endfor %}
+            </div>
+         </div>
+      </div>
+
+.. note::
+
+   Some models, such as Llama 3, require an external license agreement through
+   a third party (for example, Meta).
+
+System validation
+=================
+
+Before running AI workloads, it's important to validate that your AMD hardware is configured
+correctly and performing optimally.
+
+If you have already validated your system settings, including aspects like NUMA auto-balancing, you
+can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
+optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
+before starting training.
+
+To test for optimal performance, consult the recommended :ref:`System health benchmarks
+<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
+system's configuration.
+
+Environment setup
+=================
+
+This Docker image is optimized for specific model configurations outlined
+as follows. Performance can vary for other training workloads, as AMD
+doesn’t validate configurations and run conditions outside those described.
+
+Pull the Docker image
+---------------------
+
+Use the following command to pull the Docker image from Docker Hub.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/jax-maxtext-v25.11-benchmark-models.yaml
+
+   {% set docker = data.dockers[0] %}
+
+   .. code-block:: shell
+
+      docker pull {{ docker.pull_tag }}
+
+.. _amd-maxtext-multi-node-setup-v25.11:
+
+Multi-node configuration
+------------------------
+
+See :doc:`/how-to/rocm-for-ai/system-setup/multi-node-setup` to configure your
+environment for multi-node training.
+
+.. _amd-maxtext-get-started-v25.11:
+
+Benchmarking
+============
+
+Once the setup is complete, choose between two options to reproduce the
+benchmark results:
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/jax-maxtext-v25.11-benchmark-models.yaml
+
+   .. _vllm-benchmark-mad:
+
+   {% set docker = data.dockers[0] %}
+   {% set model_groups = data.model_groups %}
+   {% for model_group in model_groups %}
+      {% for model in model_group.models %}
+
+   .. container:: model-doc {{model.mad_tag}}
+
+      .. tab-set::
+
+         {% if model.mad_tag and "single-node" in model.doc_options %}
+         .. tab-item:: MAD-integrated benchmarking
+
+            The following run command is tailored to {{ model.model }}.
+            See :ref:`amd-maxtext-model-support-v25.11` to switch to another available model.
+
+            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
+               directory and install the required packages on the host machine.
+
+               .. code-block:: shell
+
+                  git clone https://github.com/ROCm/MAD
+                  cd MAD
+                  pip install -r requirements.txt
+
+            2. Use this command to run the performance benchmark test on the {{ model.model }} model
+               using one GPU with the :literal:`{{model.precision}}` data type on the host machine.
+
+               .. code-block:: shell
+
+                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
+                  madengine run \
+                      --tags {{model.mad_tag}} \
+                      --keep-model-dir \
+                      --live-output \
+                      --timeout 28800
+
+            MAD launches a Docker container with the name
+            ``container_ci-{{model.mad_tag}}``. The latency and throughput reports of the
+            model are collected in the following path: ``~/MAD/perf.csv/``.
+         {% endif %}
+
+         .. tab-item:: Standalone benchmarking
+
+            The following commands are optimized for {{ model.model }}. See
+            :ref:`amd-maxtext-model-support-v25.11` to switch to another
+            available model. Some instructions and resources might not be
+            available for all models and configurations.
+
+            .. rubric:: Download the Docker image and required scripts
+
+            Run the JAX MaxText benchmark tool independently by starting the
+            Docker container as shown in the following snippet.
+
+            .. code-block:: shell
+
+               docker pull {{ docker.pull_tag }}
+
+            {% if model.model_repo and "single-node" in model.doc_options %}
+            .. rubric:: Single node training
+
+            1. Set up environment variables.
+
+               .. code-block:: shell
+
+                  export MAD_SECRETS_HFTOKEN=<Your Hugging Face token>
+                  export HF_HOME=<Location of saved/cached Hugging Face models>
+
+               ``MAD_SECRETS_HFTOKEN`` is your Hugging Face access token to access models, tokenizers, and data.
+               See `User access tokens <https://huggingface.co/docs/hub/en/security-tokens>`__.
+
+               ``HF_HOME`` is where ``huggingface_hub`` will store local data. See `huggingface_hub CLI <https://huggingface.co/docs/huggingface_hub/main/en/guides/cli#huggingface-cli-download>`__.
+               If you already have downloaded or cached Hugging Face artifacts, set this variable to that path.
+               Downloaded files typically get cached to ``~/.cache/huggingface``.
+
+            2. Launch the Docker container.
+
+               .. code-block:: shell
+
+                  docker run -it \
+                      --device=/dev/dri \
+                      --device=/dev/kfd \
+                      --network host \
+                      --ipc host \
+                      --group-add video \
+                      --cap-add=SYS_PTRACE \
+                      --security-opt seccomp=unconfined \
+                      --privileged \
+                      -v $HOME:$HOME \
+                      -v $HOME/.ssh:/root/.ssh \
+                      -v $HF_HOME:/hf_cache \
+                      -e HF_HOME=/hf_cache \
+                      -e MAD_SECRETS_HFTOKEN=$MAD_SECRETS_HFTOKEN
+                      --shm-size 64G \
+                      --name training_env \
+                      {{ docker.pull_tag }}
+
+            3. In the Docker container, clone the ROCm MAD repository and navigate to the
+               benchmark scripts directory at ``MAD/scripts/jax-maxtext``.
+
+               .. code-block:: shell
+
+                  git clone https://github.com/ROCm/MAD
+                  cd MAD/scripts/jax-maxtext
+
+            4. Run the setup scripts to install libraries and datasets needed
+               for benchmarking.
+
+               .. code-block:: shell
+
+                  ./jax-maxtext_benchmark_setup.sh -m {{ model.model_repo }}
+
+            5. To run the training benchmark without quantization, use the following command:
+
+               .. code-block:: shell
+
+                  ./jax-maxtext_benchmark_report.sh -m {{ model.model_repo }}
+
+               For quantized training, run the script with the appropriate option for your Instinct GPU.
+
+               .. tab-set::
+
+                  .. tab-item:: MI355X and MI350X
+
+                     For ``fp8`` quantized training on MI355X and MI350X GPUs, use the following command:
+
+                     .. code-block:: shell
+
+                        ./jax-maxtext_benchmark_report.sh -m {{ model.model_repo }} -q fp8
+
+                  {% if model.model_repo not in ["Llama-3.1-70B", "Llama-3.3-70B"] %}
+                  .. tab-item:: MI325X and MI300X
+
+                     For ``nanoo_fp8`` quantized training on MI300X series GPUs, use the following command:
+
+                     .. code-block:: shell
+
+                        ./jax-maxtext_benchmark_report.sh -m {{ model.model_repo }} -q nanoo_fp8
+                  {% endif %}
+
+            {% endif %}
+            {% if model.multinode_training_script and "multi-node" in model.doc_options %}
+            .. rubric:: Multi-node training
+
+            The following examples use SLURM to run on multiple nodes.
+
+            .. note::
+
+               The following scripts will launch the Docker container and run the
+               benchmark. Run them outside of any Docker container.
+
+            1. Make sure ``$HF_HOME`` is set before running the test. See
+               `ROCm benchmarking <https://github.com/ROCm/MAD/blob/develop/scripts/jax-maxtext/gpu-rocm/readme.md>`__
+               for more details on downloading the Llama models before running the
+               benchmark.
+
+            2. To run multi-node training for {{ model.model }},
+               use the
+               `multi-node training script <https://github.com/ROCm/MAD/blob/develop/scripts/jax-maxtext/gpu-rocm/{{ model.multinode_training_script }}>`__
+               under the ``scripts/jax-maxtext/gpu-rocm/`` directory.
+
+            3. Run the multi-node training benchmark script.
+
+               .. code-block:: shell
+
+                  sbatch -N <num_nodes> {{ model.multinode_training_script }}
+
+            .. rubric:: Profiling with rocprofv3
+
+            If you need to collect a trace and the JAX profiler isn't working, use ``rocprofv3`` provided by the :doc:`ROCprofiler-SDK <rocprofiler-sdk:index>` as a workaround. For example:
+
+            .. code-block:: bash
+
+               rocprofv3 \
+                   --hip-trace \
+                   --kernel-trace \
+                   --memory-copy-trace \
+                   --rccl-trace \
+                   --output-format pftrace \
+                   -d ./v3_traces \ # output directory
+                   -- ./jax-maxtext_benchmark_report.sh -m {{ model.model_repo }} # or desired command
+
+            You can set the directory where you want the .json traces to be
+            saved using ``-d <TRACE_DIRECTORY>``. The resulting traces can be
+            opened in Perfetto: `<https://ui.perfetto.dev/>`__.
+
+         {% else %}
+            .. rubric:: Multi-node training
+
+            For multi-node training examples, choose a model from :ref:`amd-maxtext-model-support-v25.11`
+            with an available `multi-node training script <https://github.com/ROCm/MAD/tree/develop/scripts/jax-maxtext/gpu-rocm>`__.
+         {% endif %}
+      {% endfor %}
+   {% endfor %}
+
+Known issues
+============
+
+- Minor performance regression (< 4%) for BF16 quantization in Llama models and Mixtral 8x7b.
+
+- You might see minor loss spikes, or loss curve may have slightly higher
+  convergence end values compared to the previous ``jax-training`` image.
+
+- For FP8 training on MI355, many models will display a warning message like:
+  ``Warning: Latency not found for MI_M=16, MI_N=16, MI_K=128,
+  mi_input_type=BFloat8Float8_fnuz. Returning latency value of 32 (really
+  slow).`` The compile step may take longer than usual, but training will run.
+  This will be fixed in a future release.
+
+- The built-in JAX profiler isn't working. If you need to collect a trace and
+  the JAX profiler isn't working, use ``rocprofv3`` provided by the
+  :doc:`ROCprofiler-SDK <rocprofiler-sdk:index>` as a workaround. For example:
+
+  .. code-block:: bash
+
+     rocprofv3 \
+         --hip-trace \
+         --kernel-trace \
+         --memory-copy-trace \
+         --rccl-trace \
+         --output-format pftrace \
+         -d ./v3_traces \ # output directory
+         -- ./jax-maxtext_benchmark_report.sh -m {{ model.model_repo }} # or desired command
+
+  You can set the directory where you want the .json traces to be
+  saved using ``-d <TRACE_DIRECTORY>``. The resulting traces can be
+  opened in Perfetto: `<https://ui.perfetto.dev/>`__.
+
+Further reading
+===============
+
+- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
+
+- To learn more about system settings and management practices to configure your system for
+  AMD Instinct MI300X Series GPUs, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
+
+- For a list of other ready-made Docker images for AI with ROCm, see
+  `AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
+
+Previous versions
+=================
+
+See :doc:`jax-maxtext-history` to find documentation for previous releases
+of the ``ROCm/jax-training`` Docker image.
--- a/Show More
+++ b/Show More