PyTorch eager
LeNet5 · batch_size = 4096 · fp32 · run 01a03f0e-f83f…
●passedReported evidence
Not re-observed recently.
Primary measurement
3.93ms±0.00 · mean of 100
Rank 2 in its comparison group · source-native comparison · observed 2026-03-05
max ms 3.95 · min ms 3.91 · std ms 0.00798 · mean ms 3.93
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadbatch_size = 4096 · fp32
comparison keysha256:59f33a52078a0b81…
sourceKernelBench baseline timings
external idH100_PCIe_LambdaLabs/torch/level3/4_LeNet5.py
sha256:7968d10f98115ef4d1d636b…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
batch_size4096
input_1fp32 [4096, 1, 32, 32]
definition comparatornot_asserted
Measurements
latency · max3.95 ms · n=100
latency · mean3.93 ms · n=100
latency · min3.91 ms · n=100
latency · std7.98 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": 3930000,
"maximum": 3950000,
"minimum": 3910000,
"confidence95": [
3928436,
3931564
]
},
"primaryStatistic": "mean"
},
"observedAt": "2026-03-05T08:38:15.000Z",
"measurements": [
{
"unit": "ns",
"value": 7980,
"metric": "latency",
"statistic": "std"
}
],
"sourceNative": {
"source": "kernelbench",
"metrics": {
"max_ms": 3.95,
"min_ms": 3.91,
"std_ms": 0.00798,
"mean_ms": 3.93
},
"benchmark": "H100_PCIe_LambdaLabs/baseline_time_torch.json",
"externalId": "H100_PCIe_LambdaLabs/torch/level3/4_LeNet5.py"
},
"protocolDigest": "sha256:547725b033a9911eae0c5ac362344a85aed824894ed4ab476af945b9bb491ea6",
"workloadDigest": "sha256:2ba067811110f90bac6febf50a5b2f99c106765d6b4ce89e685bf1acc37e017f",
"environmentDigest": "sha256:89f9b28b06758f22a1df331fea58bfde2c9cc0f3627d1829509743ea538f0011",
"implementationDigest": "sha256:743740cb291e13acea425e6396f51c5eb1418919d7303c59fb26f6c4206e6f38"
},
"metadata": {
"name": "kernelbench-h100-pcie-lambdalabs-torch-l3-4-lenet5",
"title": "LeNet5 · 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": {
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"environment": {
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"software": {}
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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 LeNet5 →JSON