PyTorch eager
ShuffleNet · width = 224 · height = 224 · batch_size = 10 · input_channels = 3 · fp32 · run 01a03f0e-f840…
●passedReported evidence
Not re-observed recently.
Primary measurement
34.4ms±0.01 · mean of 100
Rank 2 in its comparison group · source-native comparison · observed 2026-03-05
max ms 34.5 · min ms 34.3 · std ms 0.047 · mean ms 34.4
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadwidth = 224 · height = 224 · batch_size = 10 · input_channels = 3 · fp32
comparison keysha256:3dfb9714892f4eaf…
sourceKernelBench baseline timings
external idH100_PCIe_LambdaLabs/torch/level3/26_ShuffleNet.py
sha256:1bdd2095643d533c3cff51b…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
width224
height224
batch_size10
input_channels3
input_1fp32 [10, 3, 224, 224]
definition comparatornot_asserted
Measurements
latency · max34.5 ms · n=100
latency · mean34.4 ms · n=100
latency · min34.3 ms · n=100
latency · std47.0 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
Show manifestHide manifest
{
"run": {
"kind": "BenchmarkRun",
"spec": {
"status": "passed",
"timing": {
"samples": 100,
"latencyNs": {
"mean": 34400000,
"maximum": 34500000,
"minimum": 34300000,
"confidence95": [
34390788,
34409212
]
},
"primaryStatistic": "mean"
},
"observedAt": "2026-03-05T08:38:15.000Z",
"measurements": [
{
"unit": "ns",
"value": 47000,
"metric": "latency",
"statistic": "std"
}
],
"sourceNative": {
"source": "kernelbench",
"metrics": {
"max_ms": 34.5,
"min_ms": 34.3,
"std_ms": 0.047,
"mean_ms": 34.4
},
"benchmark": "H100_PCIe_LambdaLabs/baseline_time_torch.json",
"externalId": "H100_PCIe_LambdaLabs/torch/level3/26_ShuffleNet.py"
},
"protocolDigest": "sha256:547725b033a9911eae0c5ac362344a85aed824894ed4ab476af945b9bb491ea6",
"workloadDigest": "sha256:35e093e12eab0ac43e117c03ca0616f409126418caae363a02b5bb5b4fe29d8d",
"environmentDigest": "sha256:89f9b28b06758f22a1df331fea58bfde2c9cc0f3627d1829509743ea538f0011",
"implementationDigest": "sha256:98d3731e83ad09172c0b552dc66090a6ce3dcc8a382b8c01c5f2cf0227f02e0b"
},
"metadata": {
"name": "kernelbench-h100-pcie-lambdalabs-torch-l3-26-shufflenet",
"title": "ShuffleNet · PyTorch eager · Lambda Labs"
},
"apiVersion": "kernelindex.dev/v1alpha1"
},
"protocol": {
"kind": "BenchmarkProtocol",
"spec": {
"harness": {
"name": "KernelBench timing scripts",
"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"
},
"apiVersion": "kernelindex.dev/v1alpha1"
},
"environment": {
"kind": "ExecutionEnvironment",
"spec": {
"hardware": {
"vendor": "nvidia",
"product": "NVIDIA H100",
"formFactor": "PCIe",
"architecture": "sm_90"
},
"software": {}
},
"metadata": {
"name": "kernelbench-h100-pcie-lambdalabs",
"title": "KernelBench host · H100 PCIe (Lambda Labs)"
},
"apiVersion": "kernelindex.dev/v1alpha1"
}
}Cite this record (permalink, digest, access date)
Report an issue with this run
Published 2026-08-26 · KernelBench baseline timings · MITAll results for ShuffleNet →JSON