Family Qwen · 235B parameters

Qwen 3 235B-A22B

235B/22B active MoE (128 experts, 8 active). AIME 2024 85.7, LiveCodeBench 70.7.

🇨🇳 Alibaba·License Apache 2.0·Context 128k tokens·Output April 2025·Tested on the GIGABYTE AI TOP ATOM · our measurements← Catalog

01What it can do

Strengths
  • Best open MoE at the time
  • Active 22B → fast for its size
  • AIME 2024 85.7
Limitations to know
  • —At least 142 GB in Q4
  • —Multi-GPU deployment or Mac Studio required
Architecture
MoE · 128 experts, 8 active · 94 layers · GQA 64Q/4KV
Training
36T tokens. Instruct-2507 and Thinking-2507 variants (July 2025).
Ideal for
Frontier reasoningAdvanced codingAgents

05Install

Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.

$ollama run qwen3:235b
⚠
First download: between 2 and 40 GB depending on the selected quantization. Plan for sufficient disk space; a stable connection is recommended. Subsequent launches are instant.

02Required memory

Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.

Q4_K_M
The lightest, ~5% loss
142 GB
Q5_K_M
Good quality/size compromise
170 GB
Q8_0
Nearly indistinguishable from FP16
250 GB
FP16
Full precision — server use
470 GB
Fallback CPU · If you don't have a GPU, allow 160 GB of RAM minimum to run this model at reduced speed.

What hardware do you need for Qwen 3 235B-A22B?

To run Qwen 3 235B-A22B locally with Q4 quantization, you need about 142 GB of VRAM. An option to compare: BOSGAME M5 128GB / 2TB (Ryzen AI Max+ 395) — this model exceeds this mini-PC's GPU capacity: choose a smaller model or suitable infrastructure.

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03Expected speed

Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.

Entry-level
~3t/s
GTX 1650, RX 6600, MBA M2 8GB
Mid-range
~12t/s
RTX 4060, 4070, MBP M3 Pro
High-end
~28t/s
RTX 4090, M4 Max, Radeon 7900

04Public benchmarks

Scores reproduced from model cards or MMLU-Pro / community sources. Unit: % correct answers.

AIME 2024
85.7
AIME 2025
81.5
LiveCodeBench v5
70.7