PyTorch

{
  "run": {
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      "status": "passed",
      "timing": {
        "latencyNs": {
          "median": 2296320
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        "primaryStatistic": "median"
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        "metrics": {
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        "benchmark": "megatron_swiglu/full",
        "externalId": "megatron_swiglu/full/torch/nvidia-h100-80gb-hbm3/batch4-ffn16384-seq2048-bf16/2026-07-26-00-09-57"
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      "environmentDigest": "sha256:2750ae381d6de582f1eb6b9008e0987463d19ede8af127d97d76c5ae09121393",
      "implementationDigest": "sha256:ebdec3855511a3b6ae7df50fa27a70da8c06b0eb5d8026a2fe27779438c068bb"
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      "title": "SwiGLU activation, Megatron layout · torch · full",
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    "apiVersion": "kernelindex.dev/v1alpha1"
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        "repository": "https://github.com/linkedin/Liger-Kernel"
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      "comparability": {
        "notes": "Timed: the forward pass plus the backward pass, via triton.testing.do_bench with quantiles 0.5/0.2/0.8 (median with p20/p80 spread). The upstream CSV records no CUDA, driver, or torch version; the Liger release rides each run's labels. Comparable only within one kernel, workload, timed pass, and GPU.",
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      "software": {}
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    "metadata": {
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      "title": "Liger benchmark host · H100 80GB HBM3"
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}