Leaderboard Β· All comparisons Β· LynxBenchAI 26Q3 Index
Device pages: Intel Core i7-10850H CPU @ 2.70GHz Β· 12th Gen Intel Core i7-1270P
On the LynxBenchAI 26Q3 Index, the Intel Core i7-10850H CPU @ 2.70GHz scores 1.16x the 12th Gen Intel Core i7-1270P overall (Training 3.59x, Inference 2.00x, Compute 0.04x). It leads on Training and Inference and trails on Compute.
| Score | Ratio | Intel Core i7-10850H CPU @ 2.70GHz | 12th Gen Intel Core i7-1270P |
|---|---|---|---|
| Overall (GT) | 1.16x | 0.34 | 0.30 |
| Training | 3.59x | 0.70 | 0.20 |
| Inference | 2.00x | 0.34 | 0.17 |
| Compute | 0.04x | 0.07 | 1.59 |
| Model | Precision | Ratio | Intel Core i7-10850H CPU @ 2.70GHz | 12th Gen Intel Core i7-1270P |
|---|---|---|---|---|
| compute | ||||
| Dense MatMul | BF16 | 0.53x | 0.00 | 0.00 |
| Dense MatMul | FP16 | 0.48x | 0.00 | 0.00 |
| Dense MatMul | FP32 | 0.05x | 0.06 | 1.25 |
| Dense MatMul | FP64 | 0.17x | 0.07 | 0.41 |
| Sparse MatMul | FP32 | 0.02x | 0.06 | 3.15 |
| Sparse MatMul | FP64 | 0.03x | 0.18 | 6.48 |
| inference | ||||
| DistilBERT | FP16 | 0.51x | 0.32 | 0.62 |
| DistilBERT | FP8 | 5.12x | 1.83 | 0.36 |
| DistilBERT | INT8 | 0.56x | 0.07 | 0.12 |
| DistilGPT2 | FP16 | 1.09x | 0.02 | 0.02 |
| DistilGPT2 | FP8 | 1.01x | 0.17 | 0.17 |
| DistilGPT2 | INT8 | 1.32x | 0.12 | 0.09 |
| Flan-T5 Small | FP16 | 1.32x | 0.32 | 0.24 |
| Flan-T5 Small | FP8 | 2.80x | 0.29 | 0.10 |
| Flan-T5 Small | INT8 | 1.69x | 0.07 | 0.04 |
| GraphGPS (Peptides) | FP16 | 22.92x | 0.46 | 0.02 |
| GraphGPS (Peptides) | FP8 | 1.02x | 0.29 | 0.28 |
| GraphGPS (Peptides) | INT8 | 0.61x | 0.07 | 0.11 |
| MobileNetV3 | FP16 | 42.11x | 0.42 | 0.01 |
| MobileNetV3 | INT8 | 2.70x | 1.15 | 0.43 |
| training | ||||
| DistilBERT | BF16 | 0.60x | 0.01 | 0.02 |
| DistilBERT | FP32 | 2.15x | 2.20 | 1.03 |
| DistilGPT2 | BF16 | n/a | not run | not run |
| DistilGPT2 | FP32 | 4.28x | 1.76 | 0.41 |
| Flan-T5 Small | BF16 | 0.69x | 0.01 | 0.02 |
| Flan-T5 Small | FP32 | 7.98x | 1.76 | 0.22 |
| GraphGPS (Peptides) | BF16 | 0.68x | 0.03 | 0.05 |
| GraphGPS (Peptides) | FP32 | 0.70x | 0.58 | 0.82 |
| MobileNetV3 | BF16 | 21.27x | 0.35 | 0.02 |
| MobileNetV3 | FP32 | 15.70x | 0.88 | 0.06 |
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.
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