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mm_fp4_mxfp4_cudnn_n4096_k4096

FlashInfer-Bench baselines · python · Apache-2.0

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Vendorable · source mirrored · Apache-2.0View source →

No package. Vendor the mirrored source: 14 lines, Apache-2.0, pinned at da91508.

main.py
curl "https://kernelindex.com/api/v1/implementations/flashinfer-mm-fp4-mxfp4-cudnn-n4096-k4096?include=source"
interfacepython
revisionda915083d4c7
symbolrun
pathmain.py
Compatibility
measured onNVIDIA B200
declared hardwareNVIDIA B200
architecturessm_100
dtypesbf16

Benchmark evidence

3 measurements across 1 GPU, fastest first.

Operation / workload
Hardware
Latency
Rank
Observed
GEMM MXFP4 n4096 k4096bf16 · [1024, 4096]
NVIDIA B200
271.3µs
#1 of 3
2026-06-06
GEMM MXFP4 n4096 k4096bf16 · [128, 4096]
NVIDIA B200
274.2µs
#1 of 3
2026-06-06
GEMM MXFP4 n4096 k4096bf16 · [16, 4096]
NVIDIA B200
277.1µs
#1 of 3
2026-06-06

Reported · How evidence levels are derived →

Source and license

sourcehttps://huggingface.co/datasets/flashinfer-ai/flashinfer-trace
commitda915083d4c7c5e61aa3005e3d17ae488e0fc71c
revision digestsha256:eb2a4571125cae507259ba560aae698c02e09e1d87c0b938b16c2d54504d1657
license declaredApache-2.0
license concludedApache-2.0
authorsbaseline
imported2026-08-16

Kernel source

main.py14 lines
import torch
import flashinfer


def run(A, B):
    a_fp4, a_sf = flashinfer.mxfp4_quantize(A)
    b_fp4, b_sf = flashinfer.mxfp4_quantize(B)
    out = torch.empty(A.shape[0], B.shape[0], device=A.device, dtype=torch.bfloat16)
    flashinfer.mm_fp4(
        a_fp4, b_fp4.T, a_sf, b_sf.T, None, torch.bfloat16, out,
        block_size=32, use_8x4_sf_layout=False, backend="cudnn", use_nvfp4=False,
    )
    return out

Source code from FlashInfer-Bench (flashinfer-ai/flashinfer-trace) · Apache-2.0

Best evidence level for this revision: reported

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