Can I Run Nemotron Nano 9B V2 (free) on a Apple M3 (16GB)?
Runs at Q8_0 — near-lossless quality.
13 quantizations fit your 16.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 10.5 GB | 12.0 GB | 9.5 GB | +5.5 GB |
| Q6_K | 8.3 GB | 9.8 GB | 9.1 GB | +7.7 GB |
| Q5_K_M | 7.3 GB | 8.8 GB | 7.1 GB | +8.7 GB |
| Q5_K_S | 7.1 GB | 8.6 GB | 6.8 GB | +8.9 GB |
| Q5_0 | 7.1 GB | 8.6 GB | 6.3 GB | +8.9 GB |
| Q4_1 | 6.6 GB | 8.1 GB | 5.8 GB | +9.4 GB |
| Q4_K_M | 6.4 GB | 7.9 GB | 6.5 GB | +9.6 GB |
| Q4_K_S | 6.1 GB | 7.6 GB | 6.2 GB | +9.9 GB |
| Q4_0 | 6.0 GB | 7.5 GB | 5.3 GB | +10.0 GB |
| Q3_K_L | 5.0 GB | 6.5 GB | 5.5 GB | +11.0 GB |
| Q3_K_M | 4.7 GB | 6.2 GB | 5.4 GB | +11.3 GB |
| Q3_K_S | 4.4 GB | 5.9 GB | 5.1 GB | +11.6 GB |
| Q2_K | 3.9 GB | 5.4 GB | 5.0 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 M3 (16GB) run Nemotron Nano 9B V2 (free)?
Yes. The Apple M3 (16GB)'s 16.0GB of unified memory is enough to run Nemotron Nano 9B V2 (free) at Q8_0 quantization (10.5GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 16.0GB. It uses about 10.5GB of memory and 12.0GB 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.