Qwen3 235B-A22B
The largest Qwen3. It needs about 145 GB to load, so it is a model for multi-GPU rigs and the biggest Macs, but with 22B active parameters it runs at a usable speed once it fits.
ollama run qwen3:235bNeeds about 144 GB of graphics memory, or unified memory on a Mac. First time? Install Ollama, then paste that into a terminal.
Download142 GB
Memory to runAbout 144 GB
Read per token13.3 GB (mixture of experts)
LicenseApache 2.0
Context32K tokens, 128K with YaRN
ReleasedApril 2025
Good for
- Hard coding and reasoning on a multi-GPU rig
- Thinking mode on long problems
- A step up when gpt-oss 120B isn't enough
Not the best pick for
- Anything under about 150 GB of memory
- Image input: it is text only
2 GPUs and Macs
What runs Qwen3 235B-A22B
Estimated speed on each card at the smallest memory size that fits it, from memory bandwidth. As a mixture-of-experts model it reads only part of its weights per token, so it runs much faster than its size suggests.
Cheapest build from our list that runs it well: Dell DSS 8440 + 10× Tesla P100, from $5,100 used, at about 19 tok/s. See the builds.
Compare
Other models
- Qwen3 4BAbout 4 GB · Quick everyday chat and summaries
- Qwen3 8BAbout 7 GB · A solid all-rounder for chat and writing
- Gemma 3 12BAbout 10 GB · Reads images as well as text
- Qwen3 14BAbout 11 GB · Noticeably smarter at reasoning and code
- gpt-oss 20BAbout 16 GB · OpenAI's open model. Fast, strong reasoning
- Gemma 3 27BAbout 19 GB · Google's best open model for its size
- Qwen3 30B-A3BAbout 21 GB · 30B smarts at small-model speed
- Qwen3 32BAbout 22 GB · Strong coding and long answers
- Llama 3.3 70BAbout 45 GB · Close to the big cloud models
- gpt-oss 120BAbout 67 GB · OpenAI's largest open model
- DeepSeek R1 671BAbout 406 GB · The full DeepSeek R1, for the biggest rigs