Leaderboard Β· All comparisons Β· LynxBenchAI 26Q3 Index
Device pages: NVIDIA H100 NVL Β· NVIDIA H100 PCIe
On the LynxBenchAI 26Q3 Index, the NVIDIA H100 NVL scores 1.14x the NVIDIA H100 PCIe overall (Training 1.22x, Inference 1.08x, Compute 1.13x).
| Score | Ratio | H100 NVL | H100 PCIe |
|---|---|---|---|
| Overall (GT) | 1.14x | 692.67 | 606.24 |
| Training | 1.22x | 588.50 | 484.04 |
| Inference | 1.08x | 1123.94 | 1039.63 |
| Compute | 1.13x | 347.75 | 306.52 |
| Model | Precision | Ratio | H100 NVL | H100 PCIe |
|---|---|---|---|---|
| compute | ||||
| Dense MatMul | BF16 | 0.93x | 715.56 | 768.57 |
| Dense MatMul | FP16 | 1.01x | 696.38 | 689.31 |
| Dense MatMul | FP32 | 1.56x | 706.80 | 453.31 |
| Dense MatMul | FP64 | 1.02x | 240.90 | 236.33 |
| Sparse MatMul | FP32 | 1.20x | 143.02 | 119.54 |
| Sparse MatMul | FP64 | 1.17x | 254.53 | 218.28 |
| inference | ||||
| DistilBERT | FP16 | 1.05x | 1714.39 | 1632.34 |
| DistilBERT | FP8 | 1.11x | 912.88 | 820.82 |
| DistilBERT | INT8 | 1.06x | 720.85 | 678.29 |
| DistilGPT2 | FP16 | 0.91x | 2084.27 | 2285.68 |
| DistilGPT2 | FP8 | 1.01x | 1194.18 | 1180.17 |
| DistilGPT2 | INT8 | 1.04x | 954.30 | 920.38 |
| Flan-T5 Small | FP16 | 1.01x | 2327.68 | 2301.17 |
| Flan-T5 Small | FP8 | 1.17x | 1356.97 | 1161.48 |
| Flan-T5 Small | INT8 | 1.15x | 947.28 | 820.62 |
| GraphGPS (Peptides) | FP16 | 1.13x | 748.98 | 663.07 |
| GraphGPS (Peptides) | FP8 | 1.10x | 368.62 | 335.28 |
| GraphGPS (Peptides) | INT8 | 1.09x | 341.34 | 311.92 |
| MobileNetV3 | FP16 | 1.18x | 2187.52 | 1853.75 |
| MobileNetV3 | INT8 | 1.24x | 692.80 | 558.19 |
| training | ||||
| DistilBERT | BF16 | 1.10x | 940.17 | 853.29 |
| DistilBERT | FP32 | 1.19x | 1081.01 | 905.29 |
| DistilGPT2 | BF16 | 1.21x | 522.66 | 433.15 |
| DistilGPT2 | FP32 | 1.15x | 903.48 | 787.73 |
| Flan-T5 Small | BF16 | 1.28x | 523.52 | 410.10 |
| Flan-T5 Small | FP32 | 1.25x | 898.75 | 719.62 |
| GraphGPS (Peptides) | BF16 | 1.36x | 357.80 | 263.19 |
| GraphGPS (Peptides) | FP32 | 1.25x | 318.48 | 255.40 |
| MobileNetV3 | BF16 | 1.15x | 243.47 | 210.90 |
| MobileNetV3 | FP32 | 1.25x | 537.67 | 431.84 |
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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