Can I run Nemotron 3 Nano 30B-A3B?

Nemotron 3 Nano is a mixture-of-experts model from NVIDIA aimed at people who want strong chat, reasoning, and coding from a local box without paying full dense-model costs. The trick is in the name: it carries 31.6B parameters total but only routes about 3.6B of them per token, so it generates at the speed of a tiny model while thinking with a much larger one. The catch every MoE newcomer learns the hard way is that you still have to load the whole thing. At a 4-bit quant that is roughly 19 GB of weights, and you want at least 32 GB of RAM, which rules out a 12 GB card like an RTX 3060 entirely.

In daily use the active-parameter trick really shows. On an RTX 4090 it streams at around 393 tokens per second, which is absurdly fast for a model this capable, and even an Apple Silicon M Max sits near 160 tokens per second on unified memory. CPU-only on DDR5 drops to about 23 tokens per second, usable for batch jobs but not interactive chat. The headline feature is the 1,000K context window, but treat that as a billboard number: at just 128K of context the full memory footprint climbs to about 50 GB, so on a 32 GB machine you are realistically working with a few tens of thousands of tokens, not a million.

Against its peers, Gemma 4 31B is the more flexible pick if you need vision, since Nemotron 3 Nano is text-only, and Granite 4.0 H Small is a comparable-size dense alternative if you would rather not deal with MoE memory quirks. What Nemotron does best is throughput-per-quality: nothing else in this size class generates this fast while still handling multi-step reasoning. One caution before you build on it: the NVIDIA Open Model license is open-weight, not true open-source, so read the terms carefully rather than assuming Apache-style freedom for commercial use.