Can I Run Llama 3.3 Nemotron Super 49B V1.5 on a Apple M1 Max (32GB)?
Runs at Q4_K_M — good quality with reasonable headroom.
7 quantizations fit your 32.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q4_K_MBEST | 31.3 GB | 32.8 GB | 30.2 GB | +0.8 GB |
| Q4_K_S | 29.6 GB | 31.1 GB | 28.6 GB | +2.4 GB |
| Q4_0 | 29.1 GB | 30.6 GB | 28.5 GB | +2.9 GB |
| Q3_K_L | 23.2 GB | 24.7 GB | 26.3 GB | +8.8 GB |
| Q3_K_M | 21.9 GB | 23.4 GB | 24.3 GB | +10.1 GB |
| Q3_K_S | 20.2 GB | 21.7 GB | 22.0 GB | +11.8 GB |
| Q2_K | 17.4 GB | 18.9 GB | 18.7 GB | +14.6 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 32.0GB.
FAQ
Can the Apple M1 Max (32GB) run Llama 3.3 Nemotron Super 49B V1.5?
Yes. The Apple M1 Max (32GB)'s 32.0GB of unified memory is enough to run Llama 3.3 Nemotron Super 49B V1.5 at Q4_K_M quantization (31.3GB required).
What's the best quantization to use?
Q4_K_M is the highest-precision quantization that fits in your 32.0GB. It uses about 31.3GB of memory and 32.8GB 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.