Can I Run Devstral Small 2 24B Instruct 2512 on a Apple M1 Pro (16GB)?
Runs at Q4_1 — good quality with reasonable headroom.
8 quantizations fit your 16.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q4_1BEST | 16.0 GB | 17.5 GB | 14.9 GB | 0 |
| Q4_K_M | 15.6 GB | 17.1 GB | 14.3 GB | +0.4 GB |
| Q4_K_S | 14.7 GB | 16.2 GB | 13.6 GB | +1.3 GB |
| Q4_0 | 14.5 GB | 16.0 GB | 13.5 GB | +1.5 GB |
| Q3_K_L | 11.7 GB | 13.2 GB | 12.4 GB | +4.3 GB |
| Q3_K_M | 11.1 GB | 12.6 GB | 11.5 GB | +4.9 GB |
| Q3_K_S | 10.2 GB | 11.7 GB | 10.4 GB | +5.8 GB |
| Q2_K | 8.9 GB | 10.4 GB | 8.9 GB | +7.1 GB |
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FAQ
Can the Apple M1 Pro (16GB) run Devstral Small 2 24B Instruct 2512?
Yes. The Apple M1 Pro (16GB)'s 16.0GB of unified memory is enough to run Devstral Small 2 24B Instruct 2512 at Q4_1 quantization (16.0GB required).
What's the best quantization to use?
Q4_1 is the highest-precision quantization that fits in your 16.0GB. It uses about 16.0GB of memory and 17.5GB 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.