Leaderboard Β· All comparisons Β· LynxBenchAI 26Q3 Index
Device pages: NVIDIA B200 Β· NVIDIA H100 80GB HBM3
On the LynxBenchAI 26Q3 Index, the NVIDIA B200 scores 1.39x the NVIDIA H100 80GB HBM3 overall (Training 1.54x, Inference 1.33x, Compute 1.25x).
| Score | Ratio | B200 | H100 80GB HBM3 |
|---|---|---|---|
| Overall (GT) | 1.39x | 1306.35 | 939.45 |
| Training | 1.54x | 1089.67 | 709.35 |
| Inference | 1.33x | 2224.55 | 1678.11 |
| Compute | 1.25x | 611.10 | 487.13 |
| Model | Precision | Ratio | B200 | H100 80GB HBM3 |
|---|---|---|---|---|
| compute | ||||
| Dense MatMul | BF16 | 1.37x | 1796.64 | 1315.06 |
| Dense MatMul | FP16 | 1.38x | 1744.13 | 1264.27 |
| Dense MatMul | FP32 | 1.62x | 1665.76 | 1026.69 |
| Dense MatMul | FP64 | 0.52x | 217.23 | 414.37 |
| Sparse MatMul | FP32 | 1.13x | 181.26 | 161.05 |
| Sparse MatMul | FP64 | 1.15x | 323.75 | 280.45 |
| inference | ||||
| DistilBERT | FP16 | 1.54x | 4010.21 | 2611.72 |
| DistilBERT | FP8 | 1.34x | 1826.30 | 1362.71 |
| DistilBERT | INT8 | 1.62x | 1765.34 | 1090.29 |
| DistilGPT2 | FP16 | 0.49x | 1860.53 | 3797.65 |
| DistilGPT2 | FP8 | 0.46x | 887.76 | 1920.23 |
| DistilGPT2 | INT8 | 0.58x | 870.08 | 1489.31 |
| Flan-T5 Small | FP16 | 1.63x | 6389.69 | 3919.63 |
| Flan-T5 Small | FP8 | 1.42x | 2357.12 | 1658.94 |
| Flan-T5 Small | INT8 | 1.70x | 2364.50 | 1389.31 |
| GraphGPS (Peptides) | FP16 | 2.15x | 2274.49 | 1060.04 |
| GraphGPS (Peptides) | FP8 | 2.01x | 1102.43 | 548.07 |
| GraphGPS (Peptides) | INT8 | 2.00x | 1039.57 | 520.52 |
| MobileNetV3 | FP16 | 1.64x | 4642.77 | 2832.98 |
| MobileNetV3 | INT8 | 1.71x | 1590.18 | 931.43 |
| training | ||||
| DistilBERT | BF16 | 1.57x | 2029.16 | 1291.98 |
| DistilBERT | FP32 | 1.62x | 2400.48 | 1480.99 |
| DistilGPT2 | BF16 | 1.47x | 960.73 | 651.85 |
| DistilGPT2 | FP32 | 1.67x | 1939.46 | 1159.01 |
| Flan-T5 Small | BF16 | 1.74x | 1085.59 | 622.63 |
| Flan-T5 Small | FP32 | 2.01x | 2202.60 | 1095.97 |
| GraphGPS (Peptides) | BF16 | 1.47x | 567.88 | 386.94 |
| GraphGPS (Peptides) | FP32 | 1.60x | 599.94 | 374.00 |
| MobileNetV3 | BF16 | 0.98x | 251.52 | 257.12 |
| MobileNetV3 | FP32 | 1.14x | 641.26 | 564.13 |
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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