Leaderboard Β· All comparisons Β· LynxBenchAI 26Q3 Index
Device pages: NVIDIA L40S Β· NVIDIA L40
On the LynxBenchAI 26Q3 Index, the NVIDIA L40S scores 1.02x the NVIDIA L40 overall (Training 1.02x, Inference 1.02x, Compute 1.02x).
| Score | Ratio | L40S | L40 |
|---|---|---|---|
| Overall (GT) | 1.02x | 395.82 | 388.24 |
| Training | 1.02x | 316.04 | 309.83 |
| Inference | 1.02x | 528.12 | 517.93 |
| Compute | 1.02x | 344.33 | 338.18 |
| Model | Precision | Ratio | L40S | L40 |
|---|---|---|---|---|
| compute | ||||
| Dense MatMul | BF16 | 0.99x | 243.02 | 245.25 |
| Dense MatMul | FP16 | 1.06x | 243.74 | 229.28 |
| Dense MatMul | FP32 | 1.37x | 303.04 | 221.64 |
| Dense MatMul | FP64 | 1.02x | 8.68 | 8.52 |
| Sparse MatMul | FP32 | 0.95x | 575.74 | 603.26 |
| Sparse MatMul | FP64 | 0.90x | 577.96 | 640.06 |
| inference | ||||
| DistilBERT | FP16 | 1.15x | 898.53 | 782.83 |
| DistilBERT | FP8 | 0.98x | 514.72 | 522.97 |
| DistilBERT | INT8 | 0.96x | 438.15 | 457.85 |
| DistilGPT2 | FP16 | 1.11x | 752.18 | 680.11 |
| DistilGPT2 | FP8 | 1.00x | 457.31 | 456.92 |
| DistilGPT2 | INT8 | 0.97x | 420.32 | 433.61 |
| Flan-T5 Small | FP16 | 1.09x | 1009.63 | 929.96 |
| Flan-T5 Small | FP8 | 0.84x | 566.59 | 674.11 |
| Flan-T5 Small | INT8 | 0.94x | 503.01 | 537.06 |
| GraphGPS (Peptides) | FP16 | 1.04x | 268.17 | 257.99 |
| GraphGPS (Peptides) | FP8 | 1.01x | 137.60 | 135.74 |
| GraphGPS (Peptides) | INT8 | 1.00x | 138.11 | 137.62 |
| MobileNetV3 | FP16 | 1.02x | 1375.69 | 1354.41 |
| MobileNetV3 | INT8 | 1.03x | 403.39 | 390.30 |
| training | ||||
| DistilBERT | BF16 | 1.03x | 405.03 | 391.85 |
| DistilBERT | FP32 | 0.93x | 492.41 | 527.44 |
| DistilGPT2 | BF16 | 1.03x | 223.21 | 215.88 |
| DistilGPT2 | FP32 | 1.02x | 391.29 | 383.61 |
| Flan-T5 Small | BF16 | 1.01x | 263.33 | 260.10 |
| Flan-T5 Small | FP32 | 0.99x | 470.80 | 474.94 |
| GraphGPS (Peptides) | BF16 | 1.02x | 184.43 | 180.11 |
| GraphGPS (Peptides) | FP32 | 1.09x | 207.50 | 190.84 |
| MobileNetV3 | BF16 | 0.98x | 248.23 | 252.36 |
| MobileNetV3 | FP32 | 1.10x | 376.00 | 342.30 |
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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