Leaderboard Β· All comparisons Β· LynxBenchAI 26Q3 Index
Device pages: Intel Graphics [0x7d55] (Intel Core Ultra 7 165H) Β· Intel Graphics (Intel Core Ultra 7 165U)
On the LynxBenchAI 26Q3 Index, the Intel Graphics [0x7d55] (Intel Core Ultra 7 165H) scores 2.10x the Intel Graphics (Intel Core Ultra 7 165U) overall (Training 1.83x, Inference 1.96x, Compute 2.46x). The two runs used different platforms (WSL and native Linux).
| Score | Ratio | Intel Graphics [0x7d55] (Intel Core Ultra 7 165H) | Intel Graphics (Intel Core Ultra 7 165U) |
|---|---|---|---|
| Overall (GT) | 2.10x | 15.82 | 7.53 |
| Training | 1.83x | 12.24 | 6.67 |
| Inference | 1.96x | 17.36 | 8.85 |
| Compute | 2.46x | 21.68 | 8.82 |
From the LynxBenchAI 26Q3 Index category scores, which are not raw throughput; a lead under 10% counts as level.
| Model | Precision | Ratio | Intel Graphics [0x7d55] (Intel Core Ultra 7 165H) | Intel Graphics (Intel Core Ultra 7 165U) |
|---|---|---|---|---|
| compute | ||||
| Dense MatMul | BF16 | 1.80x | 4.43 | 2.46 |
| Dense MatMul | FP16 | 2.70x | 6.61 | 2.45 |
| Dense MatMul | FP32 | 2.73x | 12.55 | 4.59 |
| Dense MatMul | FP64 | 2.06x | 1.01 | 0.49 |
| Sparse MatMul | FP32 | 2.49x | 116.25 | 46.61 |
| Sparse MatMul | FP64 | 2.03x | 8.54 | 4.21 |
| inference | ||||
| DistilBERT | FP16 | 2.36x | 35.01 | 14.83 |
| DistilBERT | FP8 | 12.44x | 0.20 | 0.02 |
| DistilBERT | INT8 | 2.02x | 21.65 | 10.73 |
| DistilGPT2 | FP16 | 2.53x | 40.38 | 15.94 |
| DistilGPT2 | FP8 | 10.85x | 0.28 | 0.03 |
| DistilGPT2 | INT8 | 2.20x | 21.76 | 9.90 |
| Flan-T5 Small | FP16 | 2.09x | 41.49 | 19.87 |
| Flan-T5 Small | FP8 | n/a | not run | not run |
| Flan-T5 Small | INT8 | 2.01x | 27.89 | 13.87 |
| GraphGPS (Peptides) | FP16 | 2.12x | 15.26 | 7.19 |
| GraphGPS (Peptides) | FP8 | n/a | not run | 0.22 |
| GraphGPS (Peptides) | INT8 | 0.20x | 1.04 | 5.22 |
| MobileNetV3 | FP16 | 2.15x | 44.10 | 20.55 |
| MobileNetV3 | INT8 | 1.93x | 13.06 | 6.76 |
| training | ||||
| DistilBERT | BF16 | 2.15x | 13.52 | 6.30 |
| DistilBERT | FP32 | 2.26x | 27.75 | 12.27 |
| DistilGPT2 | BF16 | 1.80x | 8.78 | 4.88 |
| DistilGPT2 | FP32 | 2.14x | 22.61 | 10.57 |
| Flan-T5 Small | BF16 | 1.88x | 10.95 | 5.81 |
| Flan-T5 Small | FP32 | 2.01x | 21.71 | 10.79 |
| GraphGPS (Peptides) | BF16 | 1.64x | 3.01 | 1.83 |
| GraphGPS (Peptides) | FP32 | 1.78x | 8.58 | 4.81 |
| MobileNetV3 | BF16 | 1.60x | 8.88 | 5.55 |
| MobileNetV3 | FP32 | 1.10x | 8.00 | 7.24 |
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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