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>
Gahow Wang
76487b7963