Can I Run Ministral 3 8B 2512 on a Apple M2 (8GB)?
Runs at Q5_K_M — good quality with reasonable headroom.
9 quantizations fit your 8.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q5_K_MBEST | 7.3 GB | 8.8 GB | 6.1 GB | +0.7 GB |
| Q5_K_S | 7.1 GB | 8.6 GB | 5.9 GB | +0.9 GB |
| Q4_1 | 6.6 GB | 8.1 GB | 5.4 GB | +1.4 GB |
| Q4_K_M | 6.4 GB | 7.9 GB | 5.2 GB | +1.6 GB |
| Q4_K_S | 6.1 GB | 7.6 GB | 5.0 GB | +1.9 GB |
| Q4_0 | 6.0 GB | 7.5 GB | 4.9 GB | +2.0 GB |
| Q3_K_M | 4.7 GB | 6.2 GB | 4.2 GB | +3.3 GB |
| Q3_K_S | 4.4 GB | 5.9 GB | 3.9 GB | +3.6 GB |
| Q2_K | 3.9 GB | 5.4 GB | 3.4 GB | +4.1 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 8.0GB.
FAQ
Can the Apple M2 (8GB) run Ministral 3 8B 2512?
Yes. The Apple M2 (8GB)'s 8.0GB of unified memory is enough to run Ministral 3 8B 2512 at Q5_K_M quantization (7.3GB required).
What's the best quantization to use?
Q5_K_M is the highest-precision quantization that fits in your 8.0GB. It uses about 7.3GB of memory and 8.8GB 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.