Can I Run gemma 4 31B on a Apple M2 (24GB)?
Runs at Q5_K_S — good quality with reasonable headroom.
7 quantizations fit your 24.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q5_K_SBEST | 23.6 GB | 25.1 GB | 21.3 GB | +0.4 GB |
| Q4_K_M | 20.8 GB | 22.3 GB | 18.7 GB | +3.2 GB |
| Q4_K_S | 19.7 GB | 21.2 GB | 17.8 GB | +4.3 GB |
| Q3_K_L | 15.6 GB | 17.1 GB | 16.6 GB | +8.4 GB |
| Q3_K_M | 14.7 GB | 16.2 GB | 15.3 GB | +9.3 GB |
| Q3_K_S | 13.6 GB | 15.1 GB | 13.8 GB | +10.4 GB |
| Q2_K | 11.8 GB | 13.3 GB | 11.9 GB | +12.3 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 24.0GB.
FAQ
Can the Apple M2 (24GB) run gemma 4 31B?
Yes. The Apple M2 (24GB)'s 24.0GB of unified memory is enough to run gemma 4 31B at Q5_K_S quantization (23.6GB required).
What's the best quantization to use?
Q5_K_S is the highest-precision quantization that fits in your 24.0GB. It uses about 23.6GB of memory and 25.1GB 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.