Can I Run Mistral Small 3 on a Apple M5 (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 | 15.8 GB | 17.3 GB | 14.9 GB | +0.3 GB |
| Q4_K_M | 15.3 GB | 16.8 GB | 14.3 GB | +0.7 GB |
| Q4_K_S | 14.5 GB | 16.0 GB | 13.6 GB | +1.5 GB |
| Q4_0 | 14.3 GB | 15.8 GB | 13.5 GB | +1.7 GB |
| Q3_K_L | 11.5 GB | 13.0 GB | 12.4 GB | +4.5 GB |
| Q3_K_M | 10.9 GB | 12.4 GB | 11.5 GB | +5.1 GB |
| Q3_K_S | 10.1 GB | 11.6 GB | 10.4 GB | +5.9 GB |
| Q2_K | 8.8 GB | 10.3 GB | 8.9 GB | +7.2 GB |
Try it in the cloud first
Don't want to download Mistral Small 3 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 16.0GB.
FAQ
Can the Apple M5 (16GB) run Mistral Small 3?
Yes. The Apple M5 (16GB)'s 16.0GB of unified memory is enough to run Mistral Small 3 at Q4_1 quantization (15.8GB required).
What's the best quantization to use?
Q4_1 is the highest-precision quantization that fits in your 16.0GB. It uses about 15.8GB of memory and 17.3GB 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.