PyTorch eager
EfficientNetB2 · batch_size = 2 · fp32 · run 01a03f0e-f840…
●passedReported evidence
Not re-observed recently.
Primary measurement
1.27ms±0.01 · mean of 100
Rank 1 in its comparison group · source-native comparison · observed 2026-03-05
max ms 1.49 · min ms 1.24 · std ms 0.0331 · mean ms 1.27
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadbatch_size = 2 · fp32
comparison keysha256:8aaf2eaf2c217473…
sourceKernelBench baseline timings
external idH100_PCIe_LambdaLabs/torch/level3/24_EfficientNetB2.py
sha256:ef5cc728cd46aa815eceefa…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
batch_size2
input_1fp32 [2, 3, 224, 224]
definition comparatornot_asserted
Measurements
latency · max1.49 ms · n=100
latency · mean1.27 ms · n=100
latency · min1.24 ms · n=100
latency · std33.1 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.
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Canonical manifest
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{
"run": {
"kind": "BenchmarkRun",
"spec": {
"status": "passed",
"timing": {
"samples": 100,
"latencyNs": {
"mean": 1270000,
"maximum": 1490000,
"minimum": 1240000,
"confidence95": [
1263512,
1276488
]
},
"primaryStatistic": "mean"
},
"observedAt": "2026-03-05T08:38:15.000Z",
"measurements": [
{
"unit": "ns",
"value": 33100,
"metric": "latency",
"statistic": "std"
}
],
"sourceNative": {
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"metrics": {
"max_ms": 1.49,
"min_ms": 1.24,
"std_ms": 0.0331,
"mean_ms": 1.27
},
"benchmark": "H100_PCIe_LambdaLabs/baseline_time_torch.json",
"externalId": "H100_PCIe_LambdaLabs/torch/level3/24_EfficientNetB2.py"
},
"protocolDigest": "sha256:547725b033a9911eae0c5ac362344a85aed824894ed4ab476af945b9bb491ea6",
"workloadDigest": "sha256:0c9bb5b49e61d634994d80aa131845987c89ebcd4caf9475543a4e371b1c1d77",
"environmentDigest": "sha256:89f9b28b06758f22a1df331fea58bfde2c9cc0f3627d1829509743ea538f0011",
"implementationDigest": "sha256:736ee33c0e382ebe54dd57bd8f6c023d91f639f18e681b78bee737627b14a6fe"
},
"metadata": {
"name": "kernelbench-h100-pcie-lambdalabs-torch-l3-24-efficientnetb2",
"title": "EfficientNetB2 · 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",
"title": "KernelBench baseline timing"
},
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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 EfficientNetB2 →JSON