Leaderboard Β· All comparisons Β· LynxBenchAI 26Q3 Index
Device pages: NVIDIA GeForce RTX 5090 (WSL) Β· NVIDIA RTX 6000 Ada Generation
On the LynxBenchAI 26Q3 Index, the NVIDIA GeForce RTX 5090 (WSL) scores 1.19x the NVIDIA RTX 6000 Ada Generation overall (Training 1.26x, Inference 1.49x, Compute 0.66x). It leads on Training and Inference and trails on Compute. The two runs used different platforms (WSL and native Linux).
| Score | Ratio | RTX 5090 (WSL) | RTX 6000 Ada Generation |
|---|---|---|---|
| Overall (GT) | 1.19x | 545.71 | 457.36 |
| Training | 1.26x | 470.34 | 373.92 |
| Inference | 1.49x | 891.80 | 597.42 |
| Compute | 0.66x | 261.39 | 396.70 |
From the LynxBenchAI 26Q3 Index category scores, which are not raw throughput; a lead under 10% counts as level.
| Model | Precision | Ratio | RTX 5090 (WSL) | RTX 6000 Ada Generation |
|---|---|---|---|---|
| compute | ||||
| Dense MatMul | BF16 | 1.42x | 424.43 | 298.13 |
| Dense MatMul | FP16 | 1.65x | 406.02 | 245.50 |
| Dense MatMul | FP32 | 0.91x | 324.34 | 356.80 |
| Dense MatMul | FP64 | 1.06x | 10.12 | 9.55 |
| Sparse MatMul | FP32 | 0.40x | 275.51 | 696.19 |
| Sparse MatMul | FP64 | 0.30x | 193.19 | 645.83 |
| inference | ||||
| DistilBERT | FP16 | 1.64x | 1455.26 | 886.10 |
| DistilBERT | FP8 | 1.72x | 994.43 | 577.39 |
| DistilBERT | INT8 | 1.61x | 855.49 | 530.42 |
| DistilGPT2 | FP16 | 1.30x | 1043.89 | 801.58 |
| DistilGPT2 | FP8 | 1.37x | 693.96 | 506.53 |
| DistilGPT2 | INT8 | 1.26x | 607.13 | 483.17 |
| Flan-T5 Small | FP16 | 1.73x | 1813.09 | 1049.28 |
| Flan-T5 Small | FP8 | 1.56x | 1151.56 | 740.46 |
| Flan-T5 Small | INT8 | 1.56x | 1025.50 | 657.38 |
| GraphGPS (Peptides) | FP16 | 1.60x | 509.96 | 318.37 |
| GraphGPS (Peptides) | FP8 | 1.62x | 271.43 | 167.34 |
| GraphGPS (Peptides) | INT8 | 1.80x | 284.01 | 157.35 |
| MobileNetV3 | FP16 | 1.32x | 2037.51 | 1547.15 |
| MobileNetV3 | INT8 | 1.18x | 549.91 | 467.17 |
| training | ||||
| DistilBERT | BF16 | 1.56x | 712.64 | 455.59 |
| DistilBERT | FP32 | 1.42x | 904.77 | 636.08 |
| DistilGPT2 | BF16 | 1.26x | 332.21 | 262.78 |
| DistilGPT2 | FP32 | 1.32x | 631.55 | 476.79 |
| Flan-T5 Small | BF16 | 1.27x | 370.23 | 292.43 |
| Flan-T5 Small | FP32 | 1.23x | 719.53 | 586.86 |
| GraphGPS (Peptides) | BF16 | 1.22x | 261.32 | 214.53 |
| GraphGPS (Peptides) | FP32 | 1.06x | 258.77 | 243.50 |
| MobileNetV3 | BF16 | 1.02x | 286.27 | 280.24 |
| MobileNetV3 | FP32 | 1.22x | 519.39 | 424.68 |
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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