Leaderboard Β· All comparisons Β· LynxBenchAI 26Q3 Index
Device pages: AMD Radeon RX 9060 XT Β· Intel Graphics [0x7d55] (Intel Core Ultra 7 165H)
On the LynxBenchAI 26Q3 Index, the AMD Radeon RX 9060 XT scores 4.82x the Intel Graphics [0x7d55] (Intel Core Ultra 7 165H) overall (Training 3.05x, Inference 7.07x, Compute 5.19x). The two runs used different platforms (native Linux and WSL).
| Score | Ratio | Radeon RX 9060 XT | Intel Graphics [0x7d55] (Intel Core Ultra 7 165H) |
|---|---|---|---|
| Overall (GT) | 4.82x | 76.32 | 15.82 |
| Training | 3.05x | 37.28 | 12.24 |
| Inference | 7.07x | 122.73 | 17.36 |
| Compute | 5.19x | 112.62 | 21.68 |
| Model | Precision | Ratio | Radeon RX 9060 XT | Intel Graphics [0x7d55] (Intel Core Ultra 7 165H) |
|---|---|---|---|---|
| compute | ||||
| Dense MatMul | BF16 | 27.35x | 121.19 | 4.43 |
| Dense MatMul | FP16 | 18.27x | 120.86 | 6.61 |
| Dense MatMul | FP32 | 0.59x | 7.42 | 12.55 |
| Dense MatMul | FP64 | 2.65x | 2.68 | 1.01 |
| Sparse MatMul | FP32 | 1.81x | 210.90 | 116.25 |
| Sparse MatMul | FP64 | 20.91x | 178.55 | 8.54 |
| inference | ||||
| DistilBERT | FP16 | 6.90x | 241.41 | 35.01 |
| DistilBERT | FP8 | 664.71x | 132.06 | 0.20 |
| DistilBERT | INT8 | 6.01x | 130.15 | 21.65 |
| DistilGPT2 | FP16 | 7.02x | 283.59 | 40.38 |
| DistilGPT2 | FP8 | 555.59x | 153.38 | 0.28 |
| DistilGPT2 | INT8 | 6.85x | 149.04 | 21.76 |
| Flan-T5 Small | FP16 | 10.45x | 433.40 | 41.49 |
| Flan-T5 Small | FP8 | n/a | 196.70 | not run |
| Flan-T5 Small | INT8 | 5.93x | 165.41 | 27.89 |
| GraphGPS (Peptides) | FP16 | 4.77x | 72.83 | 15.26 |
| GraphGPS (Peptides) | FP8 | n/a | 35.86 | not run |
| GraphGPS (Peptides) | INT8 | 34.20x | 35.55 | 1.04 |
| MobileNetV3 | FP16 | 1.90x | 83.62 | 44.10 |
| MobileNetV3 | INT8 | 2.48x | 32.33 | 13.06 |
| training | ||||
| DistilBERT | BF16 | 7.10x | 96.02 | 13.52 |
| DistilBERT | FP32 | 0.56x | 15.48 | 27.75 |
| DistilGPT2 | BF16 | 8.27x | 72.63 | 8.78 |
| DistilGPT2 | FP32 | 0.60x | 13.59 | 22.61 |
| Flan-T5 Small | BF16 | 6.94x | 75.94 | 10.95 |
| Flan-T5 Small | FP32 | 1.14x | 24.78 | 21.71 |
| GraphGPS (Peptides) | BF16 | 5.60x | 16.86 | 3.01 |
| GraphGPS (Peptides) | FP32 | 1.97x | 16.90 | 8.58 |
| MobileNetV3 | BF16 | 3.10x | 27.50 | 8.88 |
| MobileNetV3 | FP32 | 4.94x | 39.52 | 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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