PyTorch eager
Gemm ReLU Divide · batch_size = 1024 · in_features = 8192 · fp32 · run 01a03f0e-f83b…
●passedReported evidence
Not re-observed recently.
Primary measurement
4.83ms±0.01 · mean of 100
Rank 1 in its comparison group · source-native comparison · observed 2026-03-05
max ms 4.9 · min ms 4.65 · std ms 0.0543 · mean ms 4.83
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadbatch_size = 1024 · in_features = 8192 · fp32
comparison keysha256:452f7b75046cec91…
sourceKernelBench baseline timings
external idH100_PCIe_LambdaLabs/torch/level2/63_Gemm_ReLU_Divide.py
sha256:19806ea51aeb2ab409523ab…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
batch_size1024
in_features8192
input_1fp32 [1024, 8192]
definition comparatornot_asserted
Measurements
latency · max4.90 ms · n=100
latency · mean4.83 ms · n=100
latency · min4.65 ms · n=100
latency · std54.3 µs
Protocol
harnessKernelBench timing scripts
timercuda_events
primaryStatisticmean
comparabilityFamilykernelbench_baseline_timing
Environment
gpuNVIDIA H100 (sm_90)
Artifacts
No artifacts published with this run.
Replications and notes
No attestations yet.
Community attestations. They never change the evidence level; only a KernelIndex-controlled rerun does.
Add a reproduction or note
Canonical manifest
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}Cite this record (permalink, digest, access date)
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Published 2026-08-26 · KernelBench baseline timings · MITAll results for Gemm ReLU Divide →JSON