No — not on this device
Nemotron 3.5 Lightning 30B-A3B at Q4_K_M needs 18.4 GB against 10.7 GB usable, and the shortfall of 7.7 GB is more than 64 GB of system RAM can cover at a tolerable speed. A smaller sibling or a lower quantisation is the honest answer here.
Mamba-2 + MoE hybrid built for the execution layer of agents: only 6 attention blocks, so the KV cache is almost free. Weights, data and recipe all open.
What hardware do I need for Nemotron 3.5 Lightning 30B-A3B? →
The VRAM budget
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| Q8_0 | 31.3 GB | 31.9 GB | — | ~5.6 | −0.1% ppl | 21.2 GB over |
| Q6_K | 24.1 GB | 24.8 GB | — | ~7.3 | −0.4% ppl | 14.1 GB over |
| Q5_K_M | 20.9 GB | 21.5 GB | — | ~8.4 | −0.8% ppl | 10.8 GB over |
| Q4_K_M | 17.8 GB | 18.4 GB | — | ~9.9 | −1.9% ppl | 7.7 GB over |
| Q3_K_M | 14.4 GB | 15.0 GB | — | ~12 | −5.4% ppl | 4.3 GB over |
| Q2_K | 12.3 GB | 13.0 GB | — | ~14 | −15% ppl | 2.3 GB over |
Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it. Only 6 of its 52 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-3.5-Lightning-30B-A3B-BF16-4bit \ --max-tokens 512 --prompt "Hello"
Apple's own array framework. The fastest path on Apple Silicon. More on MLX.