Google Gemma 2 9B It speed on Apple M3 Pro and quantization-level VRAM fit.
Apple M3 Pro meets the minimum VRAM requirement for Q4 inference of Google Gemma 2 9B It. Review the quantization breakdown below to see how higher precision settings impact VRAM and throughput.
Apple M3 Pro can run Google Gemma 2 9B It with Q4 quantization. At approximately 15 tokens/second, you can expect Basic speed - best for non-interactive tasks.
You have 31GB headroom, which is sufficient for system overhead and smooth operation.
| Quantization | VRAM needed | VRAM available | Estimated speed | Verdict |
|---|---|---|---|---|
| Q4 | 5GB | 36GB | 15.10 tok/s | ✅ Fits comfortably |
| Q8 | 10GB | 36GB | 10.57 tok/s | ✅ Fits comfortably |
| FP16 | 19GB | 36GB | 5.74 tok/s | ✅ Fits comfortably |
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Apple M3 Pro can run Google Gemma 2 9B It at Q4 with an estimated 15 tok/s.
Q4 inference is estimated to need about 5GB VRAM on this page, while Apple M3 Pro has 36GB available.
If you need more speed or context headroom, compare alternative GPUs below and check higher-tier VRAM options.