Leaderboard Β· All comparisons Β· LynxBenchAI 26Q3 Index
Device pages: NVIDIA H100 80GB HBM3 Β· NVIDIA A100-SXM4-80GB
On the LynxBenchAI 26Q3 Index, the NVIDIA H100 80GB HBM3 scores 2.13x the NVIDIA A100-SXM4-80GB overall (Training 1.71x, Inference 2.54x, Compute 2.32x).
| Score | Ratio | H100 80GB HBM3 | A100-SXM4-80GB |
|---|---|---|---|
| Overall (GT) | 2.13x | 939.45 | 440.29 |
| Training | 1.71x | 709.35 | 415.55 |
| Inference | 2.54x | 1678.11 | 660.97 |
| Compute | 2.32x | 487.13 | 210.04 |
| Model | Precision | Ratio | H100 80GB HBM3 | A100-SXM4-80GB |
|---|---|---|---|---|
| compute | ||||
| Dense MatMul | BF16 | 2.75x | 1315.06 | 477.39 |
| Dense MatMul | FP16 | 2.93x | 1264.27 | 431.41 |
| Dense MatMul | FP32 | 3.29x | 1026.69 | 312.37 |
| Dense MatMul | FP64 | 3.13x | 414.37 | 132.43 |
| Sparse MatMul | FP32 | 1.76x | 161.05 | 91.46 |
| Sparse MatMul | FP64 | 1.72x | 280.45 | 163.00 |
| inference | ||||
| DistilBERT | FP16 | 2.06x | 2611.72 | 1267.42 |
| DistilBERT | FP8 | n/a | 1362.71 | not run |
| DistilBERT | INT8 | 1.78x | 1090.29 | 613.96 |
| DistilGPT2 | FP16 | 2.36x | 3797.65 | 1607.95 |
| DistilGPT2 | FP8 | n/a | 1920.23 | not run |
| DistilGPT2 | INT8 | 2.05x | 1489.31 | 727.01 |
| Flan-T5 Small | FP16 | 2.38x | 3919.63 | 1645.66 |
| Flan-T5 Small | FP8 | n/a | 1658.94 | not run |
| Flan-T5 Small | INT8 | 1.83x | 1389.31 | 759.30 |
| GraphGPS (Peptides) | FP16 | 1.93x | 1060.04 | 548.43 |
| GraphGPS (Peptides) | FP8 | n/a | 548.07 | not run |
| GraphGPS (Peptides) | INT8 | 1.91x | 520.52 | 272.75 |
| MobileNetV3 | FP16 | 1.66x | 2832.98 | 1709.26 |
| MobileNetV3 | INT8 | 1.85x | 931.43 | 503.53 |
| training | ||||
| DistilBERT | BF16 | 1.87x | 1291.98 | 690.91 |
| DistilBERT | FP32 | 1.84x | 1480.99 | 805.46 |
| DistilGPT2 | BF16 | 1.82x | 651.85 | 358.79 |
| DistilGPT2 | FP32 | 1.76x | 1159.01 | 657.14 |
| Flan-T5 Small | BF16 | 1.76x | 622.63 | 353.74 |
| Flan-T5 Small | FP32 | 1.67x | 1095.97 | 657.00 |
| GraphGPS (Peptides) | BF16 | 1.68x | 386.94 | 230.04 |
| GraphGPS (Peptides) | FP32 | 1.66x | 374.00 | 225.70 |
| MobileNetV3 | BF16 | 1.47x | 257.12 | 175.17 |
| MobileNetV3 | FP32 | 1.55x | 564.13 | 363.86 |
Each figure is the first device's overall score divided by the second's.
Measure your own machine: pip install lynxbench-ai Β· Install guide β
A LynxBenchAI score is sustained throughput on a fixed catalogue of PyTorch models, measured after a warm-up at a batch size raised until throughput stops improving, so it is what the machine holds under load, not its best instant. Training, Inference and Compute are three separate scores; GT combines them (Training and Inference weigh 0.4 each, Compute 0.2).
Every run uses the stock, publicly released PyTorch stack, so a result is broadly indicative of the out-of-the-box PyTorch experience on that hardware, and so of custom PyTorch models too. It does not run your own model, and it is not a vendor-tuned peak like an MLPerf submission.
Scores compare only within one Index: the 26Q3 Index fixes the models, precisions, correctness thresholds and scoring, and a later Index may change them. Each test is one timed window, the scores have no 0-100 scale, and a precision a device cannot run scores zero rather than being skipped.
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