Leaderboard Β· All comparisons Β· LynxBenchAI 26Q3 Index
Device pages: NVIDIA H100 80GB HBM3 Β· NVIDIA L40S
On the LynxBenchAI 26Q3 Index, the NVIDIA H100 80GB HBM3 scores 2.37x the NVIDIA L40S overall (Training 2.24x, Inference 3.18x, Compute 1.41x).
| Score | Ratio | H100 80GB HBM3 | L40S |
|---|---|---|---|
| Overall (GT) | 2.37x | 939.45 | 395.82 |
| Training | 2.24x | 709.35 | 316.04 |
| Inference | 3.18x | 1678.11 | 528.12 |
| Compute | 1.41x | 487.13 | 344.33 |
| Model | Precision | Ratio | H100 80GB HBM3 | L40S |
|---|---|---|---|---|
| compute | ||||
| Dense MatMul | BF16 | 5.41x | 1315.06 | 243.02 |
| Dense MatMul | FP16 | 5.19x | 1264.27 | 243.74 |
| Dense MatMul | FP32 | 3.39x | 1026.69 | 303.04 |
| Dense MatMul | FP64 | 47.72x | 414.37 | 8.68 |
| Sparse MatMul | FP32 | 0.28x | 161.05 | 575.74 |
| Sparse MatMul | FP64 | 0.49x | 280.45 | 577.96 |
| inference | ||||
| DistilBERT | FP16 | 2.91x | 2611.72 | 898.53 |
| DistilBERT | FP8 | 2.65x | 1362.71 | 514.72 |
| DistilBERT | INT8 | 2.49x | 1090.29 | 438.15 |
| DistilGPT2 | FP16 | 5.05x | 3797.65 | 752.18 |
| DistilGPT2 | FP8 | 4.20x | 1920.23 | 457.31 |
| DistilGPT2 | INT8 | 3.54x | 1489.31 | 420.32 |
| Flan-T5 Small | FP16 | 3.88x | 3919.63 | 1009.63 |
| Flan-T5 Small | FP8 | 2.93x | 1658.94 | 566.59 |
| Flan-T5 Small | INT8 | 2.76x | 1389.31 | 503.01 |
| GraphGPS (Peptides) | FP16 | 3.95x | 1060.04 | 268.17 |
| GraphGPS (Peptides) | FP8 | 3.98x | 548.07 | 137.60 |
| GraphGPS (Peptides) | INT8 | 3.77x | 520.52 | 138.11 |
| MobileNetV3 | FP16 | 2.06x | 2832.98 | 1375.69 |
| MobileNetV3 | INT8 | 2.31x | 931.43 | 403.39 |
| training | ||||
| DistilBERT | BF16 | 3.19x | 1291.98 | 405.03 |
| DistilBERT | FP32 | 3.01x | 1480.99 | 492.41 |
| DistilGPT2 | BF16 | 2.92x | 651.85 | 223.21 |
| DistilGPT2 | FP32 | 2.96x | 1159.01 | 391.29 |
| Flan-T5 Small | BF16 | 2.36x | 622.63 | 263.33 |
| Flan-T5 Small | FP32 | 2.33x | 1095.97 | 470.80 |
| GraphGPS (Peptides) | BF16 | 2.10x | 386.94 | 184.43 |
| GraphGPS (Peptides) | FP32 | 1.80x | 374.00 | 207.50 |
| MobileNetV3 | BF16 | 1.04x | 257.12 | 248.23 |
| MobileNetV3 | FP32 | 1.50x | 564.13 | 376.00 |
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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