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PyTorch eager

Gemm Multiply LeakyReLU · batch_size = 1024 · in_features = 8192 · fp32 · run 01a03f0e-f6d2…
●passedReported evidence

Not re-observed recently.

Primary measurement
2.73ms±0.00 · mean of 100

Rank 1 in its comparison group · source-native comparison · observed 2026-03-05

max ms 2.74 · min ms 2.73 · std ms 0.00146 · mean ms 2.73

Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadbatch_size = 1024 · in_features = 8192 · fp32
comparison keysha256:2083957d1699aeab…
sourceKernelBench baseline timings
external idH100_Modal/torch/level2/12_Gemm_Multiply_LeakyReLU.py
sha256:66c8cc2b1bdc10b70ecc21f…

Correctness

Marked passed by the source; the correctness policy was not published.

Workload

batch_size1024
in_features8192
input_1fp32 [1024, 8192]
definition comparatornot_asserted

Measurements

latency · max2.74 ms · n=100
latency · mean2.73 ms · n=100
latency · min2.73 ms · n=100
latency · std1.46 µs

Protocol

harnessKernelBench timing scripts
timercuda_events
primaryStatisticmean
comparabilityFamilykernelbench_baseline_timing

Environment

gpuNVIDIA H100 (sm_90)

Artifacts

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Replications and notes

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Canonical manifest

Show manifest
{
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    "kind": "BenchmarkRun",
    "spec": {
      "status": "passed",
      "timing": {
        "samples": 100,
        "latencyNs": {
          "mean": 2730000,
          "maximum": 2740000,
          "minimum": 2730000,
          "confidence95": [
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            2730286
          ]
        },
        "primaryStatistic": "mean"
      },
      "observedAt": "2026-03-05T08:38:15.000Z",
      "measurements": [
        {
          "unit": "ns",
          "value": 1460,
          "metric": "latency",
          "statistic": "std"
        }
      ],
      "sourceNative": {
        "source": "kernelbench",
        "metrics": {
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          "min_ms": 2.73,
          "std_ms": 0.00146,
          "mean_ms": 2.73
        },
        "benchmark": "H100_Modal/baseline_time_torch.json",
        "externalId": "H100_Modal/torch/level2/12_Gemm_Multiply_LeakyReLU.py"
      },
      "protocolDigest": "sha256:547725b033a9911eae0c5ac362344a85aed824894ed4ab476af945b9bb491ea6",
      "workloadDigest": "sha256:a17f76188cc0c21f9b9b8d989c6d1b575639e31039b8201cde9221ecbe2b0742",
      "environmentDigest": "sha256:2750ae381d6de582f1eb6b9008e0987463d19ede8af127d97d76c5ae09121393",
      "implementationDigest": "sha256:dabde809f49ebf87db1a4df87a322ebfc2f6ef32ce7f01dacd85988d27ac56d8"
    },
    "metadata": {
      "name": "kernelbench-h100-modal-torch-l2-12-gemm-multiply-leakyrelu",
      "title": "Gemm Multiply LeakyReLU · PyTorch eager · Modal"
    },
    "apiVersion": "kernelindex.dev/v1alpha1"
  },
  "protocol": {
    "kind": "BenchmarkProtocol",
    "spec": {
      "harness": {
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        "repository": "https://github.com/ScalingIntelligence/KernelBench"
      },
      "measurement": {
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      },
      "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"
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  "environment": {
    "kind": "ExecutionEnvironment",
    "spec": {
      "hardware": {
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        "product": "NVIDIA H100",
        "formFactor": "80GB HBM3",
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      "name": "kernelbench-h100-modal",
      "title": "KernelBench host · H100 80GB HBM3 (Modal)"
    },
    "apiVersion": "kernelindex.dev/v1alpha1"
  }
}
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