Stream a big MoE's routed experts from NVMe instead of loading them resident:
per-(layer x projection) bank keeps the stacked [E,n,k] weight on disk and preads
one expert's [n,k] slice on demand through a pinned hot-set -> per-bank LRU ->
disk (+posix_fadvise DONTNEED so the page cache stays bounded). Auto-sizes the
cache from MemAvailable (colibri cap_for_ram, auto-raises to fill big-RAM boxes)
and learns/auto-pins the hottest experts via a usage sidecar (colibri AUTOPIN).
- expert_stream.rs: ExpertStreamBank (fetch/pin/LRU/stats), registry + finalize()
(RAM-sized caps), save_usage/learning-pin. Runtime-gated (default OFF).
- QStorage::Stream variant; indexed_moe_forward gains a Stream arm sharing ONE
moe_grouped_per_expert helper with the resident path (only the fetch differs).
- gguf_file: TensorInfo::read_stream / Content::tensor_stream loader seam.
- libc made non-optional for posix_fadvise (dropped dep:libc from mkl/accelerate).
Bit-exact: streamed expert == resident bytes -> identical indexed_moe output.
Gate (tests/expert_stream_tests.rs, 4/4): streamed==resident bit-for-bit; LRU
cap bounds resident RAM with correctness cap-independent; pinned expert survives
eviction; usage sidecar learns the hot expert.
Hanzo Dev
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