Can I Run Nemotron Nano 9B V2 (free) on a Apple M3 (8GB)?
Runs at Q5_K_M — good quality with reasonable headroom.
11 quantizations fit your 8.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q5_K_MBEST | 7.3 GB | 8.8 GB | 7.1 GB | +0.7 GB |
| Q5_K_S | 7.1 GB | 8.6 GB | 6.8 GB | +0.9 GB |
| Q5_0 | 7.1 GB | 8.6 GB | 6.3 GB | +0.9 GB |
| Q4_1 | 6.6 GB | 8.1 GB | 5.8 GB | +1.4 GB |
| Q4_K_M | 6.4 GB | 7.9 GB | 6.5 GB | +1.6 GB |
| Q4_K_S | 6.1 GB | 7.6 GB | 6.2 GB | +1.9 GB |
| Q4_0 | 6.0 GB | 7.5 GB | 5.3 GB | +2.0 GB |
| Q3_K_L | 5.0 GB | 6.5 GB | 5.5 GB | +3.0 GB |
| Q3_K_M | 4.7 GB | 6.2 GB | 5.4 GB | +3.3 GB |
| Q3_K_S | 4.4 GB | 5.9 GB | 5.1 GB | +3.6 GB |
| Q2_K | 3.9 GB | 5.4 GB | 5.0 GB | +4.1 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 8.0GB.
FAQ
Can the Apple M3 (8GB) run Nemotron Nano 9B V2 (free)?
Yes. The Apple M3 (8GB)'s 8.0GB of unified memory is enough to run Nemotron Nano 9B V2 (free) at Q5_K_M quantization (7.3GB required).
What's the best quantization to use?
Q5_K_M is the highest-precision quantization that fits in your 8.0GB. It uses about 7.3GB of memory and 8.8GB 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.