PyTorch eager
Batched matrix multiplication · k = 1024 · m = 512 · n = 2048 · batch_size = 128 · fp32 · run 01a03f0e-f6f1…
●passedReported evidence
Not re-observed recently.
Primary measurement
8.17ms±0.03 · mean of 100
Rank 1 in its comparison group · source-native comparison · observed 2026-03-05
max ms 8.75 · min ms 7.93 · std ms 0.142 · mean ms 8.17
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadk = 1024 · m = 512 · n = 2048 · batch_size = 128 · fp32
comparison keysha256:c7425205c43d27e7…
sourceKernelBench baseline timings
external idH100_PCIe_LambdaLabs/torch/level1/3_Batched_matrix_multiplication.py
sha256:bdde59d33162a155899b571…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
k1024
m512
n2048
batch_size128
afp32 [128, 512, 1024]
bfp32 [128, 1024, 2048]
definition comparatornot_asserted
Measurements
latency · max8.75 ms · n=100
latency · mean8.17 ms · n=100
latency · min7.93 ms · n=100
latency · std142.0 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
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{
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"latencyNs": {
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"observedAt": "2026-03-05T08:38:15.000Z",
"measurements": [
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}Cite this record (permalink, digest, access date)
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Published 2026-08-26 · KernelBench baseline timings · MITAll results for Batched matrix multiplication →JSON