No — not on this device
Nemotron 3 Super 120B-A12B at Q4_K_M needs 70.4 GB against 24.0 GB usable, and the shortfall of 46.4 GB is more than 32 GB of system RAM can cover at a tolerable speed. A smaller sibling or a lower quantisation is the honest answer here.
The open-training-data 120B. Same hybrid layout as Lightning, so 128K context costs under a gigabyte.
What hardware do I need for Nemotron 3 Super 120B-A12B? →
Fits instead: Nemotron 3.5 Lightning 30B-A3B (18.4 GB)
The VRAM budget
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| Q8_0 | 122.7 GB | 123.4 GB | — | ~4.4 | −0.1% ppl | 99.4 GB over |
| Q6_K | 94.7 GB | 95.4 GB | — | ~5.7 | −0.4% ppl | 71.4 GB over |
| Q5_K_M | 81.8 GB | 82.5 GB | — | ~6.6 | −0.8% ppl | 58.5 GB over |
| Q4_K_M | 69.7 GB | 70.4 GB | — | ~7.7 | −1.9% ppl | 46.4 GB over |
| Q3_K_M | 56.4 GB | 57.1 GB | — | ~9.5 | −5.4% ppl | 33.1 GB over |
| Q2_K | 48.4 GB | 49.0 GB | — | ~11 | −15% ppl | 25.0 GB over |
Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it. Only 8 of its 88 blocks keep a per-token KV cache; the rest are linear-attention, Mamba or convolution blocks with a fixed-size state.
How to run it
$ pip install mlx-lm $ mlx_lm.generate --model mlx-community/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-4bit \ --max-tokens 512 --prompt "Hello"
Apple's own array framework. The fastest path on Apple Silicon. More on MLX.