PyTorch eager
Matmul with small K dimension · k = 64 · m = 32768 · n = 32768 · fp32 · run 01a03f0e-f82f…
●passedReported evidence
Not re-observed recently.
Primary measurement
6.12ms±0.04 · mean of 100
Rank 1 in its comparison group · source-native comparison · observed 2026-03-05
max ms 6.72 · min ms 5.45 · std ms 0.199 · mean ms 6.12
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadk = 64 · m = 32768 · n = 32768 · fp32
comparison keysha256:e9ded09c20687837…
sourceKernelBench baseline timings
external idH100_PCIe_LambdaLabs/torch/level1/7_Matmul_with_small_K_dimension_.py
sha256:293ad197f3cd3b60672698a…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
k64
m32768
n32768
afp32 [32768, 64]
bfp32 [64, 32768]
definition comparatornot_asserted
Measurements
latency · max6.72 ms · n=100
latency · mean6.12 ms · n=100
latency · min5.45 ms · n=100
latency · std199.0 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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"timing": {
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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 Matmul with small K dimension →JSON