Can I Run Llama 3.3 Nemotron Super 49B V1.5 on a Apple M3 Pro (36GB)?
Runs at Q5_K_S — good quality with reasonable headroom.
10 quantizations fit your 36.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q5_K_SBEST | 35.4 GB | 36.9 GB | 34.4 GB | +0.6 GB |
| Q5_0 | 35.3 GB | 36.8 GB | 34.3 GB | +0.7 GB |
| Q4_1 | 32.2 GB | 33.7 GB | 31.4 GB | +3.8 GB |
| Q4_K_M | 31.3 GB | 32.8 GB | 30.2 GB | +4.8 GB |
| Q4_K_S | 29.6 GB | 31.1 GB | 28.6 GB | +6.4 GB |
| Q4_0 | 29.1 GB | 30.6 GB | 28.5 GB | +6.9 GB |
| Q3_K_L | 23.2 GB | 24.7 GB | 26.3 GB | +12.8 GB |
| Q3_K_M | 21.9 GB | 23.4 GB | 24.3 GB | +14.1 GB |
| Q3_K_S | 20.2 GB | 21.7 GB | 22.0 GB | +15.8 GB |
| Q2_K | 17.4 GB | 18.9 GB | 18.7 GB | +18.6 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 36.0GB.
FAQ
Can the Apple M3 Pro (36GB) run Llama 3.3 Nemotron Super 49B V1.5?
Yes. The Apple M3 Pro (36GB)'s 36.0GB of unified memory is enough to run Llama 3.3 Nemotron Super 49B V1.5 at Q5_K_S quantization (35.4GB required).
What's the best quantization to use?
Q5_K_S is the highest-precision quantization that fits in your 36.0GB. It uses about 35.4GB of memory and 36.9GB 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.