Can I Run Qwen3 4B Thinking 2507 on a Apple M2 (24GB)?
Runs at full precision (fp16). Zero quality loss.
13 quantizations fit your 24.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 9.0 GB | 10.5 GB | 8.1 GB | +15.0 GB |
| Q8_0 | 5.3 GB | 6.8 GB | 4.3 GB | +18.8 GB |
| Q6_K | 4.3 GB | 5.8 GB | 3.3 GB | +19.7 GB |
| Q5_K_M | 3.8 GB | 5.3 GB | 2.9 GB | +20.2 GB |
| Q5_K_S | 3.8 GB | 5.3 GB | 2.8 GB | +20.2 GB |
| Q4_1 | 3.5 GB | 5.0 GB | 2.6 GB | +20.5 GB |
| Q4_K_M | 3.4 GB | 4.9 GB | 2.5 GB | +20.6 GB |
| Q4_K_S | 3.3 GB | 4.8 GB | 2.4 GB | +20.7 GB |
| Q4_0 | 3.3 GB | 4.8 GB | 2.4 GB | +20.8 GB |
| Q3_K_L | 2.8 GB | 4.3 GB | 2.2 GB | +21.2 GB |
| Q3_K_M | 2.7 GB | 4.2 GB | 2.1 GB | +21.3 GB |
| Q3_K_S | 2.5 GB | 4.0 GB | 1.9 GB | +21.5 GB |
| Q2_K | 2.3 GB | 3.8 GB | 1.7 GB | +21.7 GB |
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Top-ranked open-source models that fit in 24.0GB.
FAQ
Can the Apple M2 (24GB) run Qwen3 4B Thinking 2507?
Yes. The Apple M2 (24GB)'s 24.0GB of unified memory is enough to run Qwen3 4B Thinking 2507 at fp16 quantization (9.0GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 24.0GB. It uses about 9.0GB of memory and 10.5GB 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.