KernelBench hard · RTX PRO 6000

W4A16 GEMM GPT-5.6 Sol

manually audited: clean

Genuine fused W4A16 kernel pair on the native Blackwell datacenter path. Unlike the H100 sibling (SM90a cooperative mainloop + a patched mixed_input_utils.hpp), this run instantiates CUTLASS 3.x's SM100 mixed-input UMMA collectives unpatched: KernelTmaWarpSpecialized2SmMixedInputSm100 (256x128x128 tile, 2x1x1 cluster) for M>=128 and the 1Sm variant (128x64x128) for 1<M<128, with Sm100MixedInputBlockwiseScaleConfig<1,128> feeding per-128-K bf16 scales through the mainloop, built -gencode arch=compute_100a,code=sm_100a from a sparse upstream NVIDIA/cutlass clone. The asymmetric zero-point is handled by algebra instead of a library patch: packed uint4 bytes are XOR 0x88 recentred to signed int4 (q-8) and the additive term folded into bias=(8-zero)*scale, so CUTLASS's stock q_signed*scale+bias affine reproduces the reference dequant. Decode (M=1) is a hand-written CUDA GEMV: 32x32 block, each threadIdx.y warp-slice owns one 128-K quant group (64 packed bytes), bf16x2 nibble dequant, coalesced N-contiguous byte loads, shared-memory 32x33 cross-group reduction. Dequant + matmul run fused on live inputs every forward; no forbidden ops (no bitsandbytes, no marlin, no F.linear). peak_fraction 0.1021 (decode ~970 GB/s vs ~8 TB/s HBM3e) is physically plausible, nowhere near the >>1.0 signature of a cached-output lookup. template_mutated=false.

20260721_185035_codex_gpt-5.6-sol_07_w4a16_gemm