Can I run Qwen 3 30B-A3B?

Qwen 3 30B-A3B is a mixture-of-experts model, and that one fact shapes everything about how you should think of it. It has 30.5B parameters on paper, but only 3.3B of them activate per token, so it runs at the speed of a tiny model while reasoning with the breadth of a large one. The catch is memory: you still have to hold the whole thing in RAM. A 4-bit quant is about 18.5 GB and the model wants at least 32 GB to breathe, which rules out a 12 GB card like the RTX 3060 but fits a 32 GB Apple Silicon Mac comfortably, or a 24 GB 4090 if you keep context modest.

In daily use the MoE design pays off in a way the spec sheet undersells. On a 4090 you can see around 428 tokens per second, and even an M-series Max stays brisk at roughly 174 tok/s, far faster than a dense 30B would ever manage on the same hardware. CPU-only on DDR5 drops to about 25 tok/s, usable for batch jobs but not interactive chat. The 128K context is genuine, but watch the budget: filling it pushes total memory to about 49.1 GB, so on a 32 GB machine you keep working context modest and lean on a smaller quant rather than chasing the full window.

Against Gemma 4 31B, which sits at a near-identical 30.7B parameters, the trade is clear. Gemma is dense and adds coding and vision, so it tends to feel stronger on multimodal and structured code work, while Qwen 3 30B-A3B's sparse design makes it dramatically faster per token for chat and reasoning at this size class. That speed-for-its-footprint is its standout trait. It also ships under Apache 2.0, so unlike provider-specific open-weight terms you can use it freely in commercial and production work with no licensing asterisks.