PyTorch eager
Gemm Divide Sum Scaling · batch_size = 1024 · input_size = 8192 · fp32 · run 01a03f0e-f6d2…
●passedReported evidence
Not re-observed recently.
Primary measurement
2.75ms±0.00 · mean of 100
Rank 1 in its comparison group · source-native comparison · observed 2026-03-05
max ms 2.77 · min ms 2.75 · std ms 0.00372 · mean ms 2.75
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadbatch_size = 1024 · input_size = 8192 · fp32
comparison keysha256:5a6850cbeaf93698…
sourceKernelBench baseline timings
external idH100_Modal/torch/level2/14_Gemm_Divide_Sum_Scaling.py
sha256:0e8e45d6974d867a9da934f…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
batch_size1024
input_size8192
input_1fp32 [1024, 8192]
definition comparatornot_asserted
Measurements
latency · max2.77 ms · n=100
latency · mean2.75 ms · n=100
latency · min2.75 ms · n=100
latency · std3.72 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": 2750000,
"maximum": 2770000,
"minimum": 2750000,
"confidence95": [
2750000,
2750729
]
},
"primaryStatistic": "mean"
},
"observedAt": "2026-03-05T08:38:15.000Z",
"measurements": [
{
"unit": "ns",
"value": 3720,
"metric": "latency",
"statistic": "std"
}
],
"sourceNative": {
"source": "kernelbench",
"metrics": {
"max_ms": 2.77,
"min_ms": 2.75,
"std_ms": 0.00372,
"mean_ms": 2.75
},
"benchmark": "H100_Modal/baseline_time_torch.json",
"externalId": "H100_Modal/torch/level2/14_Gemm_Divide_Sum_Scaling.py"
},
"protocolDigest": "sha256:547725b033a9911eae0c5ac362344a85aed824894ed4ab476af945b9bb491ea6",
"workloadDigest": "sha256:28368d547fa1a81af3fbce9273548e930017009db2cc60ef2a4d08e97941ef25",
"environmentDigest": "sha256:2750ae381d6de582f1eb6b9008e0987463d19ede8af127d97d76c5ae09121393",
"implementationDigest": "sha256:3773eaecb04b62a5ddbe14a43ed0aee3ac87f889ee4bba940a963dbd90e2be7d"
},
"metadata": {
"name": "kernelbench-h100-modal-torch-l2-14-gemm-divide-sum-scaling",
"title": "Gemm Divide Sum Scaling · PyTorch eager · Modal"
},
"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": "80GB HBM3",
"architecture": "sm_90"
},
"software": {}
},
"metadata": {
"name": "kernelbench-h100-modal",
"title": "KernelBench host · H100 80GB HBM3 (Modal)"
},
"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 Gemm Divide Sum Scaling →JSON