huangpustar/metainfer/int8-w8a8-quantized-gemm-optimization

INT8 W8A8 GEMM — skill family router

The former single "int8-w8a8-quantized-gemm-optimization" skill was split by execution phase (2026-08-23) because decode and prefill have non-transferable recipes, different bottlenecks, and different acceptance protocols. Load the following instead:

• int8-w8a8-gemm-foundations — operator/layout contract, DUMMA and

epilogue rules, Graph-safe PyTorch interface, SGLang integration, fair benchmarking, shared correctness gates and guardrails, E2E evidence.

• int8-w8a8-gemm-decode — split-K partial kernels + fused combine,

M=1..16 validated recipes, M=16<M<=32 boundary guidance, µs-scale acceptance with noise tolerance, decode evidence and rejected ideas.

• int8-w8a8-gemm-prefill — 2D M-tile staged-LDS kernels, no split-K,

M=3072 / M=4096 validated recipes and evidence, occupancy/LDS acceptance, prefill rejected ideas.

Routing boundary: M <= 32 -> decode, M > 32 -> prefill. M in (32, 128] (decode side) and (32, 3072) (prefill side) are measured-boundary gaps — route by measurement and keep both an arm per regime until measured.

No tuning content lives in this router; always load the foundations skill plus the matching phase skill.