The plan-cache fix removed the per-expert heuristic churn but still issued one
cublasLtMatmul per expert: ~768 tiny launches per decoded token (16 local
experts × 2 GEMMs × 24 layers), which capped the FP8 decode win at ~1.05× over
BF16. Collapse each MoE GEMM into ONE strided-batched cuBLASLt FP8 matmul
(BATCH_COUNT + strided-batch offsets on all four layouts) → ~48 launches/token.
A single strided call can't carry a per-batch scalar B-scale, so the per-expert
weight scale moves out of the GEMM epilogue into a fused post-scale kernel
(rowwise_scale_moe_bf16) that applies a_scale[token]·b_scale[expert] in one
pass. This is precision-equivalent: BF16's relative error is scale-invariant, so
scaling the unscaled GEMM output afterward loses nothing vs scaling in-epilogue.
Measured on dash5 (gpt-oss-20b, TP=2, 5090), warm-server GSM8K:
decode TPOT 17.45 → 13.08 ms (FP8 now 1.41× vs BF16 18.39 ms),
throughput 57.3 → 76.4 tok/s, accuracy unchanged (FP8 91.0% vs BF16 90.0%).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Replace the W8A16 dequant→BF16-GEMM path with native FP8×FP8→BF16 GEMM
using cuBLASLt on Blackwell (RTX 5090). Both weights (static FP8 E4M3)
and activations (dynamically quantized per-row) are processed directly
on FP8 tensor cores.
Key implementation details:
- cuBLASLt on Blackwell requires transA=T for FP8, so expert weights
are transposed during model loading ([E,K,N] → [E,N,K])
- Per-row activation quantization kernel (absmax/448 → FP8 E4M3)
- Post-GEMM row-wise rescaling recovers per-token precision
- Per-expert loop (not batched) due to cuBLASLt FP8 scale constraints
The same FP8 quantized model files work — no re-quantization needed.
Activation quantization happens dynamically at inference time.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
