Google Gemma 2 27B 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 27B It. Review the quantization breakdown below to see how higher precision settings impact VRAM and throughput.
Apple M3 Pro can run Google Gemma 2 27B It with Q4 quantization. At approximately 11 tokens/second, you can expect Basic speed - best for non-interactive tasks.
You have 22GB headroom, which is sufficient for system overhead and smooth operation.
| Quantization | VRAM needed | VRAM available | Estimated speed | Verdict |
|---|---|---|---|---|
| Q4 | 14GB | 36GB | 11.08 tok/s | ✅ Fits comfortably |
| Q8 | 28GB | 36GB | 7.75 tok/s | ✅ Fits comfortably |
| FP16 | 55GB | 36GB | 4.21 tok/s | ❌ Not recommended |
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Apple M3 Pro can run Google Gemma 2 27B It at Q4 with an estimated 11 tok/s.
Q4 inference is estimated to need about 14GB 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.