Leaderboard Β· All comparisons Β· LynxBenchAI 26Q3 Index
Device pages: Apple M1 (Apple M1) Β· AMD Radeon 780M Graphics (AMD Ryzen 7 8700G w/ Radeon 780M Graphics)
On the LynxBenchAI 26Q3 Index, the Apple M1 (Apple M1) scores n/a the AMD Radeon 780M Graphics (AMD Ryzen 7 8700G w/ Radeon 780M Graphics) overall (Training n/a, Inference n/a, Compute n/a). The two runs used different platforms (macOS and native Linux).
| Score | Ratio | Apple M1 (Apple M1) | Radeon 780M Graphics (AMD Ryzen 7 8700G w/ Radeon 780M Graphics) |
|---|---|---|---|
| Overall (GT) | n/a | 4.35 | 0.00 |
| Training | n/a | 4.01 | 0.00 |
| Inference | n/a | 3.65 | 0.00 |
| Compute | n/a | 7.15 | 0.00 |
From the LynxBenchAI 26Q3 Index category scores, which are not raw throughput; a lead under 10% counts as level.
| Model | Precision | Ratio | Apple M1 (Apple M1) | Radeon 780M Graphics (AMD Ryzen 7 8700G w/ Radeon 780M Graphics) |
|---|---|---|---|---|
| compute | ||||
| Dense MatMul | BF16 | n/a | 1.92 | not run |
| Dense MatMul | FP16 | n/a | 3.83 | not run |
| Dense MatMul | FP32 | n/a | 6.81 | not run |
| Dense MatMul | FP64 | n/a | not run | not run |
| Sparse MatMul | FP32 | n/a | 30.25 | not run |
| Sparse MatMul | FP64 | n/a | not run | not run |
| inference | ||||
| DistilBERT | FP16 | n/a | 3.47 | not run |
| DistilBERT | FP8 | n/a | not run | not run |
| DistilBERT | INT8 | n/a | not run | not run |
| DistilGPT2 | FP16 | n/a | 4.17 | not run |
| DistilGPT2 | FP8 | n/a | not run | not run |
| DistilGPT2 | INT8 | n/a | not run | not run |
| Flan-T5 Small | FP16 | n/a | 19.46 | not run |
| Flan-T5 Small | FP8 | n/a | not run | not run |
| Flan-T5 Small | INT8 | n/a | not run | not run |
| GraphGPS (Peptides) | FP16 | n/a | 7.86 | not run |
| GraphGPS (Peptides) | FP8 | n/a | not run | not run |
| GraphGPS (Peptides) | INT8 | n/a | not run | not run |
| MobileNetV3 | FP16 | n/a | 24.40 | not run |
| MobileNetV3 | INT8 | n/a | 9.71 | not run |
| training | ||||
| DistilBERT | BF16 | n/a | 4.68 | not run |
| DistilBERT | FP32 | n/a | 13.76 | not run |
| DistilGPT2 | BF16 | n/a | not run | not run |
| DistilGPT2 | FP32 | n/a | not run | not run |
| Flan-T5 Small | BF16 | n/a | 4.68 | not run |
| Flan-T5 Small | FP32 | n/a | 11.38 | not run |
| GraphGPS (Peptides) | BF16 | n/a | 1.65 | not run |
| GraphGPS (Peptides) | FP32 | n/a | 5.57 | not run |
| MobileNetV3 | BF16 | n/a | 5.61 | not run |
| MobileNetV3 | FP32 | n/a | 12.60 | not run |
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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