PyTorch eager
Gemm Swish Divide Clamp Tanh Clamp · batch_size = 1024 · in_features = 8192 · fp32 · run 01a03f0e-f6d7…
●passedReported evidence
Not re-observed recently.
Primary measurement
2.83ms±0.00 · mean of 100
Rank 2 in its comparison group · source-native comparison · observed 2026-03-05
max ms 2.84 · min ms 2.82 · std ms 0.00763 · mean ms 2.83
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadbatch_size = 1024 · in_features = 8192 · fp32
comparison keysha256:3d15a1693258d4d6…
sourceKernelBench baseline timings
external idH100_Modal/torch/level2/81_Gemm_Swish_Divide_Clamp_Tanh_Clamp.py
sha256:aa39aafb7bda1bef94f132f…
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 · max2.84 ms · n=100
latency · mean2.83 ms · n=100
latency · min2.82 ms · n=100
latency · std7.63 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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{
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"latencyNs": {
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},
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},
"observedAt": "2026-03-05T08:38:15.000Z",
"measurements": [
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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 Swish Divide Clamp Tanh Clamp →JSON