PyTorch eager
GELU · dim = 393216 · batch_size = 4096 · fp32 · run 01a03f0e-f831…
●passedReported evidence
Not re-observed recently.
Primary measurement
6.95ms±0.00 · mean of 100
Rank 1 in its comparison group · source-native comparison · observed 2026-03-05
max ms 6.96 · min ms 6.95 · std ms 0.0028 · mean ms 6.95
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloaddim = 393216 · batch_size = 4096 · fp32
comparison keysha256:325f6acae541bea2…
sourceKernelBench baseline timings
external idH100_PCIe_LambdaLabs/torch/level1/26_GELU_.py
sha256:e820687594824f782634529…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
dim393216
batch_size4096
xfp32 [4096, 393216]
definition comparatornot_asserted
Measurements
latency · max6.96 ms · n=100
latency · mean6.95 ms · n=100
latency · min6.95 ms · n=100
latency · std2.80 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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{
"run": {
"kind": "BenchmarkRun",
"spec": {
"status": "passed",
"timing": {
"samples": 100,
"latencyNs": {
"mean": 6950000,
"maximum": 6960000,
"minimum": 6950000,
"confidence95": [
6950000,
6950549
]
},
"primaryStatistic": "mean"
},
"observedAt": "2026-03-05T08:38:15.000Z",
"measurements": [
{
"unit": "ns",
"value": 2800,
"metric": "latency",
"statistic": "std"
}
],
"sourceNative": {
"source": "kernelbench",
"metrics": {
"max_ms": 6.96,
"min_ms": 6.95,
"std_ms": 0.0028,
"mean_ms": 6.95
},
"benchmark": "H100_PCIe_LambdaLabs/baseline_time_torch.json",
"externalId": "H100_PCIe_LambdaLabs/torch/level1/26_GELU_.py"
},
"protocolDigest": "sha256:547725b033a9911eae0c5ac362344a85aed824894ed4ab476af945b9bb491ea6",
"workloadDigest": "sha256:fa7fb8ab69c6908c29135a549b5fe1b3b8d031482749f2216ca483614ffd3f06",
"environmentDigest": "sha256:89f9b28b06758f22a1df331fea58bfde2c9cc0f3627d1829509743ea538f0011",
"implementationDigest": "sha256:253ec6e3969bd131bbf4dcc2f2c30da1ae218dd90fc7d56f402d4d8cfd34f2b6"
},
"metadata": {
"name": "kernelbench-h100-pcie-lambdalabs-torch-l1-26-gelu",
"title": "GELU · PyTorch eager · Lambda Labs"
},
"apiVersion": "kernelindex.dev/v1alpha1"
},
"protocol": {
"kind": "BenchmarkProtocol",
"spec": {
"harness": {
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"repository": "https://github.com/ScalingIntelligence/KernelBench"
},
"measurement": {
"timer": "cuda_events",
"primaryStatistic": "mean"
},
"comparability": {
"notes": "Mean of 100 timed forward passes (CUDA events, warm-up excluded) of the reference module on fixed inputs; the 95% interval is the mean's, from the reported standard deviation. The upstream JSON records no torch or CUDA version. Comparable only within one problem and one timing host.",
"family": "kernelbench_baseline_timing"
}
},
"metadata": {
"name": "kernelbench-timing-v1",
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},
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},
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"title": "KernelBench host · H100 PCIe (Lambda Labs)"
},
"apiVersion": "kernelindex.dev/v1alpha1"
}
}Cite this record (permalink, digest, access date)
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Published 2026-08-26 · KernelBench baseline timings · MITAll results for GELU →JSON