Can I Run Nemotron Nano 12B 2 VL (free) on a Apple M2 Pro (16GB)?
Runs at Q8_0 — near-lossless quality.
6 quantizations fit your 16.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 15.0 GB | 16.5 GB | 13.1 GB | +1.0 GB |
| Q6_K | 11.9 GB | 13.4 GB | 10.1 GB | +4.1 GB |
| Q5_K_M | 10.4 GB | 11.9 GB | 8.8 GB | +5.6 GB |
| Q4_K_M | 9.0 GB | 10.5 GB | 7.5 GB | +7.0 GB |
| Q3_K_M | 6.5 GB | 8.0 GB | 6.0 GB | +9.5 GB |
| Q2_K | 5.3 GB | 6.8 GB | 4.7 GB | +10.7 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 M2 Pro (16GB) run Nemotron Nano 12B 2 VL (free)?
Yes. The Apple M2 Pro (16GB)'s 16.0GB of unified memory is enough to run Nemotron Nano 12B 2 VL (free) at Q8_0 quantization (15.0GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 16.0GB. It uses about 15.0GB of memory and 16.5GB 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.