Can I Run Ministral 3 14B 2512 on a Apple M2 Pro (16GB)?
Runs at Q8_0 — near-lossless quality.
11 quantizations fit your 16.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 15.8 GB | 17.3 GB | 14.4 GB | +0.2 GB |
| Q6_K | 12.4 GB | 13.9 GB | 11.1 GB | +3.6 GB |
| Q5_K_M | 10.9 GB | 12.4 GB | 9.6 GB | +5.1 GB |
| Q5_K_S | 10.6 GB | 12.1 GB | 9.4 GB | +5.4 GB |
| Q4_1 | 9.7 GB | 11.2 GB | 8.6 GB | +6.3 GB |
| Q4_K_M | 9.4 GB | 10.9 GB | 8.2 GB | +6.6 GB |
| Q4_K_S | 9.0 GB | 10.5 GB | 7.8 GB | +7.0 GB |
| Q4_0 | 8.8 GB | 10.3 GB | 7.8 GB | +7.2 GB |
| Q3_K_M | 6.8 GB | 8.3 GB | 6.7 GB | +9.2 GB |
| Q3_K_S | 6.3 GB | 7.8 GB | 6.1 GB | +9.7 GB |
| Q2_K | 5.6 GB | 7.1 GB | 5.3 GB | +10.4 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 16.0GB.
FAQ
Can the Apple M2 Pro (16GB) run Ministral 3 14B 2512?
Yes. The Apple M2 Pro (16GB)'s 16.0GB of unified memory is enough to run Ministral 3 14B 2512 at Q8_0 quantization (15.8GB required).
What's the best quantization to use?
Q8_0 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.