Family Qwen · 235B parameters

Qwen 3 VL 235B-A22B

Flagship vision Qwen 3 VL. 235B/22B active MoE. 256k ctx (extendable to 1M).

🇨🇳 Alibaba·License Apache 2.0·Context 256k tokens·Output May 2025← Catalog

01What it can do

Strengths
  • Best open-weight multimodal vision model (May 2025)
  • 262k context
  • Apache 2.0
Limitations to know
  • —142 GB VRAM Q4 — multi-GPU required
Architecture
Vision MoE · 235B total / 22B active · Qwen3-VL flagship
Training
Qwen3-VL 235B — text, images, video, 262k native context.
Ideal for
Frontier visionDocument analysis

04Install

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-vl: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 VL 235B-A22B?

To run Qwen 3 VL 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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This model in your private ChatGPT, without the cloud

Too large for your machine? The kit gives you the model that fits in your VRAM

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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