Can I run Qwen 3.5 35B-A3B?

Qwen 3.5 35B-A3B is a mixture-of-experts model with a useful trick at its core: of its 35B total parameters, only about 3B are active per token. So it generates at the speed of a tiny model while drawing on the knowledge of a big one. The catch is memory. You still hold the whole model in RAM, so plan around the full footprint, not the active slice. At 4-bit it lands near 21 GB, and you want at least 32 GB of system memory. A 12 GB RTX 3060 will not fit it, so realistically this is a 24 GB GPU or high-memory Apple Silicon machine.

Once it fits, the MoE design pays off and it feels quick for its weight class. On an RTX 4090 you can see around 471 tokens per second, and on an Apple M Max roughly 192, both fast enough that the answer outpaces your reading. CPU on DDR5 manages about 28 tokens per second, slow but usable for batch work. It handles chat, reasoning, coding, and vision, with a generous 256K context window. Treat that ceiling carefully though: at 128K context the total memory footprint climbs to about 53.8 GB, so long-context sessions need a genuinely large machine, not just enough to load the weights.

Against Command R 35B, a same-size dense model worth comparing, the real difference is the MoE architecture rather than the parameter count: same nominal size, but Qwen tends to run far faster per token because only 3B are active. That speed-to-capability ratio is its standout trait, getting near-instant generation out of a model with 35B worth of knowledge, plus the multimodal vision support. The smaller Qwen 3 0.6B and 1.7B are the picks if you are memory-constrained and only need basic chat. Licensing is the easy part: Apache 2.0 means you can use it freely, including commercially and in production, with no provider-specific terms to read.