Can I Run Qwen3 Coder 30B A3B Instruct on a Apple M2 Max (32GB)?
Runs comfortably at Q6_K — minimal quality loss.
11 quantizations fit your 32.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q6_KBEST | 26.1 GB | 27.6 GB | 25.1 GB | +5.9 GB |
| Q5_K_M | 22.7 GB | 24.2 GB | 21.7 GB | +9.3 GB |
| Q5_K_S | 22.1 GB | 23.6 GB | 21.1 GB | +9.9 GB |
| Q4_1 | 20.1 GB | 21.6 GB | 19.2 GB | +11.9 GB |
| Q4_K_M | 19.5 GB | 21.0 GB | 18.6 GB | +12.5 GB |
| Q4_K_S | 18.5 GB | 20.0 GB | 17.5 GB | +13.5 GB |
| Q4_0 | 18.2 GB | 19.7 GB | 17.4 GB | +13.8 GB |
| Q3_K_L | 14.6 GB | 16.1 GB | 14.6 GB | +17.4 GB |
| Q3_K_M | 13.8 GB | 15.3 GB | 14.7 GB | +18.2 GB |
| Q3_K_S | 12.7 GB | 14.2 GB | 13.3 GB | +19.3 GB |
| Q2_K | 11.0 GB | 12.5 GB | 11.3 GB | +21.0 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 32.0GB.
FAQ
Can the Apple M2 Max (32GB) run Qwen3 Coder 30B A3B Instruct?
Yes. The Apple M2 Max (32GB)'s 32.0GB of unified memory is enough to run Qwen3 Coder 30B A3B Instruct at Q6_K quantization (26.1GB required).
What's the best quantization to use?
Q6_K is the highest-precision quantization that fits in your 32.0GB. It uses about 26.1GB of memory and 27.6GB 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.