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submission 396744

gau.nernst · python · License unknown

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Vendorable · source mirrored · license unknownView source →

No package. Vendor the mirrored source: 20 lines, June 9 Researcher Reciprocity License v1.0.

submission_ref.py
curl "https://kernelindex.com/api/v1/implementations/kernelbot-nvfp4-group-gemm-396744?include=source"
interfacepython
Compatibility
measured onNVIDIA B200
declared hardwareNVIDIA B200
architecturessm_100
dtypesfp8_e4m3, nvfp4

Benchmark evidence

1 measurement across 1 GPU, fastest first.

Operation / workload
Hardware
Latency
Rank
Observed
NVFP4 group GEMMsuite of 4 cases
NVIDIA B200
184.0µs
#247 of 310
2026-01-24

Reported · How evidence levels are derived →

Source and license

sourceavailable
revision digestsha256:f43feb5c6d116d6c919ab564cd9cd4a2b1682f6dbc300d15c85bedd963515a86
license declaredunknown
license concludedunknown
authorsgau.nernst
imported2026-08-15

Kernel source

submission_ref.py20 lines
#!POPCORN leaderboard nvfp4_group_gemm
#!POPCORN gpu B200

import torch
from task import input_t, output_t


def custom_kernel(data: input_t) -> output_t:
    abc_list, _, sf_list, _ = data
    return [
        torch._scaled_mm(
            a[..., 0],
            b[..., 0].T,
            sfa.permute(5, 2, 4, 0, 1, 3).view(-1),
            sfb.permute(5, 2, 4, 0, 1, 3).view(-1),
            out_dtype=torch.float16,
        ).unsqueeze(-1)
        for (a, b, _), (sfa, sfb) in zip(abc_list, sf_list)
    ]

Source code from GPU Mode and the KernelBot dataset · June 9 Researcher Reciprocity License v1.0

Best evidence level for this revision: reported

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