Leaderboard Β· All comparisons Β· LynxBenchAI 26Q3 Index
Device pages: NVIDIA RTX 2000 Ada Generation Laptop GPU Β· Intel Graphics [0x7d55] (Intel Core Ultra 7 165H)
On the LynxBenchAI 26Q3 Index, the NVIDIA RTX 2000 Ada Generation Laptop GPU scores 4.73x the Intel Graphics [0x7d55] (Intel Core Ultra 7 165H) overall (Training 4.96x, Inference 6.44x, Compute 2.91x).
| Score | Ratio | RTX 2000 Ada Generation Laptop GPU | Intel Graphics [0x7d55] (Intel Core Ultra 7 165H) |
|---|---|---|---|
| Overall (GT) | 4.73x | 74.79 | 15.82 |
| Training | 4.96x | 60.71 | 12.24 |
| Inference | 6.44x | 111.76 | 17.36 |
| Compute | 2.91x | 63.14 | 21.68 |
From the LynxBenchAI 26Q3 Index category scores, which are not raw throughput; a lead under 10% counts as level.
| Model | Precision | Ratio | RTX 2000 Ada Generation Laptop GPU | Intel Graphics [0x7d55] (Intel Core Ultra 7 165H) |
|---|---|---|---|---|
| compute | ||||
| Dense MatMul | BF16 | 11.57x | 51.26 | 4.43 |
| Dense MatMul | FP16 | 7.56x | 50.03 | 6.61 |
| Dense MatMul | FP32 | 3.57x | 44.82 | 12.55 |
| Dense MatMul | FP64 | 1.33x | 1.34 | 1.01 |
| Sparse MatMul | FP32 | 0.95x | 110.74 | 116.25 |
| Sparse MatMul | FP64 | 10.92x | 93.25 | 8.54 |
| inference | ||||
| DistilBERT | FP16 | 5.64x | 197.50 | 35.01 |
| DistilBERT | FP8 | 496.42x | 98.62 | 0.20 |
| DistilBERT | INT8 | 4.74x | 102.57 | 21.65 |
| DistilGPT2 | FP16 | 3.94x | 159.21 | 40.38 |
| DistilGPT2 | FP8 | 306.95x | 84.74 | 0.28 |
| DistilGPT2 | INT8 | 3.96x | 86.21 | 21.76 |
| Flan-T5 Small | FP16 | 5.05x | 209.73 | 41.49 |
| Flan-T5 Small | FP8 | n/a | 126.12 | not run |
| Flan-T5 Small | INT8 | 3.70x | 103.21 | 27.89 |
| GraphGPS (Peptides) | FP16 | 4.22x | 64.47 | 15.26 |
| GraphGPS (Peptides) | FP8 | n/a | 30.56 | not run |
| GraphGPS (Peptides) | INT8 | 31.88x | 33.14 | 1.04 |
| MobileNetV3 | FP16 | 5.98x | 263.55 | 44.10 |
| MobileNetV3 | INT8 | 4.97x | 64.85 | 13.06 |
| training | ||||
| DistilBERT | BF16 | 5.65x | 76.34 | 13.52 |
| DistilBERT | FP32 | 3.31x | 91.89 | 27.75 |
| DistilGPT2 | BF16 | 5.10x | 44.78 | 8.78 |
| DistilGPT2 | FP32 | 3.21x | 72.62 | 22.61 |
| Flan-T5 Small | BF16 | 4.30x | 47.08 | 10.95 |
| Flan-T5 Small | FP32 | 3.93x | 85.35 | 21.71 |
| GraphGPS (Peptides) | BF16 | 12.69x | 38.21 | 3.01 |
| GraphGPS (Peptides) | FP32 | 3.97x | 34.04 | 8.58 |
| MobileNetV3 | BF16 | 5.88x | 52.20 | 8.88 |
| MobileNetV3 | FP32 | 10.11x | 80.92 | 8.00 |
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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