Can I Run Qwen3 VL 8B Instruct on a Apple M1 (16GB)?
Runs at Q8_0 — near-lossless quality.
11 quantizations fit your 16.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 10.3 GB | 11.8 GB | 8.7 GB | +5.7 GB |
| Q6_K | 8.3 GB | 9.8 GB | 6.7 GB | +7.8 GB |
| Q5_K_M | 7.3 GB | 8.8 GB | 5.8 GB | +8.8 GB |
| Q5_K_S | 7.1 GB | 8.6 GB | 5.7 GB | +8.9 GB |
| Q4_1 | 6.5 GB | 8.0 GB | 5.3 GB | +9.5 GB |
| Q4_K_M | 6.3 GB | 7.8 GB | 5.0 GB | +9.7 GB |
| Q4_K_S | 6.0 GB | 7.5 GB | 4.8 GB | +10.0 GB |
| Q4_0 | 6.0 GB | 7.5 GB | 4.8 GB | +10.1 GB |
| Q3_K_M | 4.7 GB | 6.2 GB | 4.1 GB | +11.3 GB |
| Q3_K_S | 4.4 GB | 5.9 GB | 3.8 GB | +11.6 GB |
| Q2_K | 3.9 GB | 5.4 GB | 3.3 GB | +12.1 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 M1 (16GB) run Qwen3 VL 8B Instruct?
Yes. The Apple M1 (16GB)'s 16.0GB of unified memory is enough to run Qwen3 VL 8B Instruct at Q8_0 quantization (10.3GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 16.0GB. It uses about 10.3GB of memory and 11.8GB 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.