Can I Run Gemma 4 31B on a Apple M2 Pro (32GB)?
Runs comfortably at Q6_K — minimal quality loss.
9 quantizations fit your 32.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q6_KBEST | 26.8 GB | 28.3 GB | 25.2 GB | +5.2 GB |
| Q5_K_M | 23.2 GB | 24.7 GB | 21.7 GB | +8.8 GB |
| Q5_K_S | 22.6 GB | 24.1 GB | 21.1 GB | +9.4 GB |
| Q4_1 | 20.6 GB | 22.1 GB | 19.1 GB | +11.4 GB |
| Q4_K_M | 20.0 GB | 21.5 GB | 18.3 GB | +12.0 GB |
| Q4_K_S | 18.9 GB | 20.4 GB | 17.4 GB | +13.1 GB |
| Q4_0 | 18.6 GB | 20.1 GB | 17.3 GB | +13.4 GB |
| Q3_K_M | 14.1 GB | 15.6 GB | 14.7 GB | +17.9 GB |
| Q3_K_S | 13.1 GB | 14.6 GB | 13.2 GB | +18.9 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 32.0GB.
FAQ
Can the Apple M2 Pro (32GB) run Gemma 4 31B?
Yes. The Apple M2 Pro (32GB)'s 32.0GB of unified memory is enough to run Gemma 4 31B at Q6_K quantization (26.8GB required).
What's the best quantization to use?
Q6_K is the highest-precision quantization that fits in your 32.0GB. It uses about 26.8GB of memory and 28.3GB recommended for comfortable inference.
What if I need more headroom for context length?
KV cache memory grows with context length. The numbers above assume a baseline 2K-4K context. For long-context use (32K+), add another 2-6GB depending on the model architecture.