PyTorch eager
Mamba2ReturnY · d_head = 64 · n_heads = 8 · batch_size = 2048 · seq_length = 128 · fp32 · run 01a03f0e-f6dd…
●passedReported evidence
Not re-observed recently.
Primary measurement
7.98ms±0.00 · mean of 100
Rank 2 in its comparison group · source-native comparison · observed 2026-03-05
max ms 8.2 · min ms 7.97 · std ms 0.0239 · mean ms 7.98
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadd_head = 64 · n_heads = 8 · batch_size = 2048 · seq_length = 128 · fp32
comparison keysha256:f17cf8e7d8598002…
sourceKernelBench baseline timings
external idH100_Modal/torch/level3/48_Mamba2ReturnY.py
sha256:0066c6ad8b3e8181d0ae15c…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
d_head64
n_heads8
batch_size2048
seq_length128
input_1fp32 [2048, 128, 8, 64]
definition comparatornot_asserted
Measurements
latency · max8.20 ms · n=100
latency · mean7.98 ms · n=100
latency · min7.97 ms · n=100
latency · std23.9 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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{
"run": {
"kind": "BenchmarkRun",
"spec": {
"status": "passed",
"timing": {
"samples": 100,
"latencyNs": {
"mean": 7980000,
"maximum": 8200000,
"minimum": 7970000,
"confidence95": [
7975316,
7984684
]
},
"primaryStatistic": "mean"
},
"observedAt": "2026-03-05T08:38:15.000Z",
"measurements": [
{
"unit": "ns",
"value": 23900,
"metric": "latency",
"statistic": "std"
}
],
"sourceNative": {
"source": "kernelbench",
"metrics": {
"max_ms": 8.2,
"min_ms": 7.97,
"std_ms": 0.0239,
"mean_ms": 7.98
},
"benchmark": "H100_Modal/baseline_time_torch.json",
"externalId": "H100_Modal/torch/level3/48_Mamba2ReturnY.py"
},
"protocolDigest": "sha256:547725b033a9911eae0c5ac362344a85aed824894ed4ab476af945b9bb491ea6",
"workloadDigest": "sha256:2d47c2207accd0df62d9c3db4b4fc1292cd48da3a70f9b55e67f3bd561db3b9d",
"environmentDigest": "sha256:2750ae381d6de582f1eb6b9008e0987463d19ede8af127d97d76c5ae09121393",
"implementationDigest": "sha256:c41ee0f148a3d86e25df0f9dff4bbe0d0d1acf5ab0152400d071ea36a306b4ff"
},
"metadata": {
"name": "kernelbench-h100-modal-torch-l3-48-mamba2returny",
"title": "Mamba2ReturnY · 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": {
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"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)
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Published 2026-08-26 · KernelBench baseline timings · MITAll results for Mamba2ReturnY →JSON