Can I Run Mistral Small 3.2 24B on a Apple M1 Pro (16GB)?
Runs at Q4_1 — good quality with reasonable headroom.
7 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_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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Top-ranked open-source models that fit in 16.0GB.
FAQ
Can the Apple M1 Pro (16GB) run Mistral Small 3.2 24B?
Yes. The Apple M1 Pro (16GB)'s 16.0GB of unified memory is enough to run Mistral Small 3.2 24B 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.