Leaderboard Β· All comparisons Β· LynxBenchAI 26Q3 Index
Device pages: NVIDIA A100-SXM4-80GB Β· NVIDIA A100 80GB PCIe
On the LynxBenchAI 26Q3 Index, the NVIDIA A100-SXM4-80GB scores 1.09x the NVIDIA A100 80GB PCIe overall (Training 1.07x, Inference 1.13x, Compute 1.04x).
| Score | Ratio | A100-SXM4-80GB | A100 80GB PCIe |
|---|---|---|---|
| Overall (GT) | 1.09x | 440.29 | 403.38 |
| Training | 1.07x | 415.55 | 387.30 |
| Inference | 1.13x | 660.97 | 584.07 |
| Compute | 1.04x | 210.04 | 201.12 |
| Model | Precision | Ratio | A100-SXM4-80GB | A100 80GB PCIe |
|---|---|---|---|---|
| compute | ||||
| Dense MatMul | BF16 | 1.14x | 477.39 | 418.74 |
| Dense MatMul | FP16 | 1.12x | 431.41 | 384.97 |
| Dense MatMul | FP32 | 0.98x | 312.37 | 317.87 |
| Dense MatMul | FP64 | 1.12x | 132.43 | 117.84 |
| Sparse MatMul | FP32 | 0.99x | 91.46 | 92.16 |
| Sparse MatMul | FP64 | 0.99x | 163.00 | 163.89 |
| inference | ||||
| DistilBERT | FP16 | 1.15x | 1267.42 | 1102.80 |
| DistilBERT | FP8 | n/a | not run | not run |
| DistilBERT | INT8 | 1.15x | 613.96 | 531.81 |
| DistilGPT2 | FP16 | 1.16x | 1607.95 | 1390.64 |
| DistilGPT2 | FP8 | n/a | not run | not run |
| DistilGPT2 | INT8 | 1.15x | 727.01 | 630.82 |
| Flan-T5 Small | FP16 | 1.17x | 1645.66 | 1411.06 |
| Flan-T5 Small | FP8 | n/a | not run | not run |
| Flan-T5 Small | INT8 | 1.13x | 759.30 | 672.11 |
| GraphGPS (Peptides) | FP16 | 1.04x | 548.43 | 525.67 |
| GraphGPS (Peptides) | FP8 | n/a | not run | not run |
| GraphGPS (Peptides) | INT8 | 1.05x | 272.75 | 260.48 |
| MobileNetV3 | FP16 | 1.16x | 1709.26 | 1472.07 |
| MobileNetV3 | INT8 | 1.10x | 503.53 | 459.72 |
| training | ||||
| DistilBERT | BF16 | 1.08x | 690.91 | 639.53 |
| DistilBERT | FP32 | 1.09x | 805.46 | 736.08 |
| DistilGPT2 | BF16 | 1.04x | 358.79 | 343.98 |
| DistilGPT2 | FP32 | 1.08x | 657.14 | 609.72 |
| Flan-T5 Small | BF16 | 1.05x | 353.74 | 336.21 |
| Flan-T5 Small | FP32 | 1.13x | 657.00 | 579.06 |
| GraphGPS (Peptides) | BF16 | 1.07x | 230.04 | 214.16 |
| GraphGPS (Peptides) | FP32 | 1.06x | 225.70 | 212.64 |
| MobileNetV3 | BF16 | 1.01x | 175.17 | 173.31 |
| MobileNetV3 | FP32 | 1.05x | 363.86 | 346.28 |
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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