Leaderboard Β· All comparisons Β· LynxBenchAI 26Q3 Index
Device pages: NVIDIA H100 80GB HBM3 Β· NVIDIA RTX PRO 6000 Blackwell Server Edition
On the LynxBenchAI 26Q3 Index, the NVIDIA H100 80GB HBM3 scores 1.31x the NVIDIA RTX PRO 6000 Blackwell Server Edition overall (Training 1.21x, Inference 1.40x, Compute 1.33x).
| Score | Ratio | H100 80GB HBM3 | RTX PRO 6000 Blackwell Server Edition |
|---|---|---|---|
| Overall (GT) | 1.31x | 939.45 | 718.86 |
| Training | 1.21x | 709.35 | 587.80 |
| Inference | 1.40x | 1678.11 | 1201.04 |
| Compute | 1.33x | 487.13 | 366.56 |
From the LynxBenchAI 26Q3 Index category scores, which are not raw throughput; a lead under 10% counts as level.
| Model | Precision | Ratio | H100 80GB HBM3 | RTX PRO 6000 Blackwell Server Edition |
|---|---|---|---|---|
| compute | ||||
| Dense MatMul | BF16 | 1.72x | 1315.06 | 764.15 |
| Dense MatMul | FP16 | 1.69x | 1264.27 | 746.60 |
| Dense MatMul | FP32 | 1.38x | 1026.69 | 744.41 |
| Dense MatMul | FP64 | 36.65x | 414.37 | 11.31 |
| Sparse MatMul | FP32 | 0.60x | 161.05 | 266.83 |
| Sparse MatMul | FP64 | 1.42x | 280.45 | 198.15 |
| inference | ||||
| DistilBERT | FP16 | 1.26x | 2611.72 | 2078.75 |
| DistilBERT | FP8 | 0.99x | 1362.71 | 1380.25 |
| DistilBERT | INT8 | 0.95x | 1090.29 | 1153.13 |
| DistilGPT2 | FP16 | 2.19x | 3797.65 | 1733.83 |
| DistilGPT2 | FP8 | 1.97x | 1920.23 | 976.68 |
| DistilGPT2 | INT8 | 1.56x | 1489.31 | 953.43 |
| Flan-T5 Small | FP16 | 1.50x | 3919.63 | 2611.39 |
| Flan-T5 Small | FP8 | 1.31x | 1658.94 | 1263.89 |
| Flan-T5 Small | INT8 | 1.27x | 1389.31 | 1093.17 |
| GraphGPS (Peptides) | FP16 | 1.78x | 1060.04 | 594.99 |
| GraphGPS (Peptides) | FP8 | 1.85x | 548.07 | 295.47 |
| GraphGPS (Peptides) | INT8 | 1.81x | 520.52 | 287.57 |
| MobileNetV3 | FP16 | 0.95x | 2832.98 | 2973.56 |
| MobileNetV3 | INT8 | 1.27x | 931.43 | 731.67 |
| training | ||||
| DistilBERT | BF16 | 1.50x | 1291.98 | 861.84 |
| DistilBERT | FP32 | 1.22x | 1480.99 | 1212.77 |
| DistilGPT2 | BF16 | 1.44x | 651.85 | 451.21 |
| DistilGPT2 | FP32 | 1.34x | 1159.01 | 867.54 |
| Flan-T5 Small | BF16 | 1.26x | 622.63 | 494.04 |
| Flan-T5 Small | FP32 | 1.21x | 1095.97 | 904.71 |
| GraphGPS (Peptides) | BF16 | 1.15x | 386.94 | 336.85 |
| GraphGPS (Peptides) | FP32 | 1.15x | 374.00 | 325.28 |
| MobileNetV3 | BF16 | 0.85x | 257.12 | 302.88 |
| MobileNetV3 | FP32 | 1.03x | 564.13 | 548.88 |
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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The Personal Edition submits your benchmark results to TechnoLynx's servers as part of participating in the public leaderboard. If that isn't acceptable for your use case, contact TechnoLynx about Press, Pro, or Enterprise licenses.
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