Leaderboard Β· All comparisons Β· LynxBenchAI 26Q3 Index
Device pages: AMD Ryzen 5 9600X Β· Intel Core i7-10850H CPU @ 2.70GHz
On the LynxBenchAI 26Q3 Index, the AMD Ryzen 5 9600X scores 15.16x the Intel Core i7-10850H CPU @ 2.70GHz overall (Training 12.22x, Inference 7.94x, Compute 96.62x). The two runs used different platforms (native Linux and WSL).
| Score | Ratio | AMD Ryzen 5 9600X | Intel Core i7-10850H CPU @ 2.70GHz |
|---|---|---|---|
| Overall (GT) | 15.16x | 5.20 | 0.34 |
| Training | 12.22x | 8.58 | 0.70 |
| Inference | 7.94x | 2.69 | 0.34 |
| Compute | 96.62x | 6.32 | 0.07 |
From the LynxBenchAI 26Q3 Index category scores, which are not raw throughput; a lead under 10% counts as level.
| Model | Precision | Ratio | AMD Ryzen 5 9600X | Intel Core i7-10850H CPU @ 2.70GHz |
|---|---|---|---|---|
| compute | ||||
| Dense MatMul | BF16 | 2636.00x | 6.64 | 0.00 |
| Dense MatMul | FP16 | 2.91x | 0.01 | 0.00 |
| Dense MatMul | FP32 | 61.01x | 3.53 | 0.06 |
| Dense MatMul | FP64 | 41.94x | 2.97 | 0.07 |
| Sparse MatMul | FP32 | 176.44x | 11.06 | 0.06 |
| Sparse MatMul | FP64 | 70.73x | 12.87 | 0.18 |
| inference | ||||
| DistilBERT | FP16 | 6.77x | 2.16 | 0.32 |
| DistilBERT | FP8 | 0.99x | 1.81 | 1.83 |
| DistilBERT | INT8 | 40.43x | 2.67 | 0.07 |
| DistilGPT2 | FP16 | 2.09x | 0.05 | 0.02 |
| DistilGPT2 | FP8 | 9.41x | 1.61 | 0.17 |
| DistilGPT2 | INT8 | 18.77x | 2.17 | 0.12 |
| Flan-T5 Small | FP16 | 4.92x | 1.57 | 0.32 |
| Flan-T5 Small | FP8 | 3.97x | 1.15 | 0.29 |
| Flan-T5 Small | INT8 | 21.16x | 1.47 | 0.07 |
| GraphGPS (Peptides) | FP16 | 6.26x | 2.90 | 0.46 |
| GraphGPS (Peptides) | FP8 | 6.48x | 1.87 | 0.29 |
| GraphGPS (Peptides) | INT8 | 39.94x | 2.81 | 0.07 |
| MobileNetV3 | FP16 | 6.18x | 2.60 | 0.42 |
| MobileNetV3 | INT8 | 11.95x | 13.79 | 1.15 |
| training | ||||
| DistilBERT | BF16 | 746.52x | 8.78 | 0.01 |
| DistilBERT | FP32 | 4.05x | 8.92 | 2.20 |
| DistilGPT2 | BF16 | n/a | 7.90 | not run |
| DistilGPT2 | FP32 | 4.61x | 8.10 | 1.76 |
| Flan-T5 Small | BF16 | 476.76x | 6.87 | 0.01 |
| Flan-T5 Small | FP32 | 4.89x | 8.58 | 1.76 |
| GraphGPS (Peptides) | BF16 | 180.81x | 5.84 | 0.03 |
| GraphGPS (Peptides) | FP32 | 10.87x | 6.27 | 0.58 |
| MobileNetV3 | BF16 | 35.88x | 12.44 | 0.35 |
| MobileNetV3 | FP32 | 19.41x | 17.09 | 0.88 |
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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