Leaderboard Β· All comparisons Β· LynxBenchAI 26Q3 Index
Device pages: NVIDIA A40 Β· NVIDIA GeForce RTX 3080
On the LynxBenchAI 26Q3 Index, the NVIDIA A40 scores 1.15x the NVIDIA GeForce RTX 3080 overall (Training 1.15x, Inference 1.03x, Compute 1.47x).
| Score | Ratio | A40 | RTX 3080 |
|---|---|---|---|
| Overall (GT) | 1.15x | 184.52 | 160.52 |
| Training | 1.15x | 187.96 | 163.70 |
| Inference | 1.03x | 248.17 | 240.49 |
| Compute | 1.47x | 95.41 | 64.77 |
| Model | Precision | Ratio | A40 | RTX 3080 |
|---|---|---|---|---|
| compute | ||||
| Dense MatMul | BF16 | 1.99x | 146.46 | 73.72 |
| Dense MatMul | FP16 | 2.02x | 144.11 | 71.21 |
| Dense MatMul | FP32 | 1.91x | 209.02 | 109.63 |
| Dense MatMul | FP64 | 1.04x | 3.48 | 3.34 |
| Sparse MatMul | FP32 | 1.11x | 84.89 | 76.72 |
| Sparse MatMul | FP64 | 1.10x | 58.80 | 53.43 |
| inference | ||||
| DistilBERT | FP16 | 1.32x | 527.07 | 398.20 |
| DistilBERT | FP8 | n/a | not run | not run |
| DistilBERT | INT8 | 1.00x | 277.68 | 278.09 |
| DistilGPT2 | FP16 | 1.29x | 523.46 | 404.76 |
| DistilGPT2 | FP8 | n/a | not run | not run |
| DistilGPT2 | INT8 | 1.06x | 248.53 | 235.15 |
| Flan-T5 Small | FP16 | 1.24x | 640.51 | 515.98 |
| Flan-T5 Small | FP8 | n/a | not run | not run |
| Flan-T5 Small | INT8 | 0.85x | 286.79 | 338.76 |
| GraphGPS (Peptides) | FP16 | 0.83x | 182.67 | 220.48 |
| GraphGPS (Peptides) | FP8 | n/a | not run | not run |
| GraphGPS (Peptides) | INT8 | 0.85x | 98.21 | 115.37 |
| MobileNetV3 | FP16 | 0.88x | 682.36 | 775.12 |
| MobileNetV3 | INT8 | 0.89x | 190.06 | 213.32 |
| training | ||||
| DistilBERT | BF16 | 1.36x | 290.50 | 213.29 |
| DistilBERT | FP32 | 1.46x | 368.16 | 252.88 |
| DistilGPT2 | BF16 | 1.16x | 162.03 | 139.11 |
| DistilGPT2 | FP32 | 1.31x | 283.93 | 216.26 |
| Flan-T5 Small | BF16 | 1.08x | 131.34 | 122.02 |
| Flan-T5 Small | FP32 | 1.19x | 240.41 | 201.41 |
| GraphGPS (Peptides) | BF16 | 0.95x | 89.45 | 93.72 |
| GraphGPS (Peptides) | FP32 | 1.14x | 88.52 | 77.55 |
| MobileNetV3 | BF16 | 0.97x | 150.47 | 155.13 |
| MobileNetV3 | FP32 | 0.90x | 217.79 | 242.26 |
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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