Can I Run Qwen3 30B A3B Thinking 2507 on a Apple M1 Pro (32GB)?
Runs comfortably at Q6_K — minimal quality loss.
10 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_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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Top-ranked open-source models that fit in 32.0GB.
FAQ
Can the Apple M1 Pro (32GB) run Qwen3 30B A3B Thinking 2507?
Yes. The Apple M1 Pro (32GB)'s 32.0GB of unified memory is enough to run Qwen3 30B A3B Thinking 2507 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.