Can I Run Mistral Small 3.2 24B on a Apple M3 Pro (18GB)?
Runs at Q5_K_S — good quality with reasonable headroom.
8 quantizations fit your 18.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q5_K_SBEST | 17.6 GB | 19.1 GB | 16.3 GB | +0.4 GB |
| Q4_1 | 16.0 GB | 17.5 GB | 14.9 GB | +2.0 GB |
| Q4_K_M | 15.6 GB | 17.1 GB | 14.3 GB | +2.4 GB |
| Q4_K_S | 14.7 GB | 16.2 GB | 13.6 GB | +3.3 GB |
| Q4_0 | 14.5 GB | 16.0 GB | 13.5 GB | +3.5 GB |
| Q3_K_M | 11.1 GB | 12.6 GB | 11.5 GB | +6.9 GB |
| Q3_K_S | 10.2 GB | 11.7 GB | 10.4 GB | +7.8 GB |
| Q2_K | 8.9 GB | 10.4 GB | 8.9 GB | +9.1 GB |
Try it in the cloud first
Don't want to download Mistral Small 3.2 24B just to try it? Use a hosted API or rent a GPU by the second.
Affiliate links — we earn a commission at no cost to you.
All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 18.0GB.
FAQ
Can the Apple M3 Pro (18GB) run Mistral Small 3.2 24B?
Yes. The Apple M3 Pro (18GB)'s 18.0GB of unified memory is enough to run Mistral Small 3.2 24B at Q5_K_S quantization (17.6GB required).
What's the best quantization to use?
Q5_K_S is the highest-precision quantization that fits in your 18.0GB. It uses about 17.6GB of memory and 19.1GB 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.