Qwen3 VL 235B A22B Thinking — Hardware Requirements & GPU Compatibility
Vision
Qwen3-VL-235B-A22B-Thinking is the reasoning-enhanced edition of Alibaba's Qwen3-VL vision-language family, a mixture-of-experts model with roughly 22.8 billion active parameters out of about 235.7 billion total, that reasons step by step before answering on both text and visual inputs. Compared to the Instruct edition, Thinking emphasizes deeper multimodal reasoning for STEM, math, and causal analysis. The model can operate PC and mobile GUIs as a visual agent, generate code from images or video, judge spatial relationships and 3D layouts, and read OCR text in 32 languages. Given its scale, it needs a multi-GPU server to run even quantized. Context length is 262,144 tokens natively, expandable to 1,048,576 tokens. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in September 2025.
11.3K downloads
405 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 3
- Parameters
- 235.7B
- Architecture
- Qwen3VLMoeForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-09-22
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen3 VL 235B A22B Thinking Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 100.7 GB | 125.7 GB | 100.16 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 103.6 GB | 128.6 GB | 103.11 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 115.4 GB | 140.4 GB | 114.89 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 118.3 GB | 143.4 GB | 117.84 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 141.9 GB | 166.9 GB | 141.40 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 168.4 GB | 193.4 GB | 167.91 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 194.9 GB | 220.0 GB | 194.43 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 236.2 GB | 261.2 GB | 235.67 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3 VL 235B A22B Thinking?
Q4_K_M · 141.9 GBQwen3 VL 235B A22B Thinking (Q4_K_M) requires 141.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 185+ GB is recommended. Using the full 262K context window can add up to 25.0 GB, bringing total usage to 166.9 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Qwen3 VL 235B A22B Thinking?
Q4_K_M · 141.9 GB6 devices with unified memory can run Qwen3 VL 235B A22B Thinking, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio (M3 Ultra, 256GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightWhere to Download Qwen3 VL 235B A22B Thinking
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Qwen3 VL 235B A22B Thinking need?
Qwen3 VL 235B A22B Thinking requires 141.9 GB of VRAM at Q4_K_M, or 471.8 GB at BF16. Full 262K context adds up to 25.0 GB (166.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 235.7B × 4.8 bits ÷ 8 = 141.4 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 25.5 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M141.9 GBQ4_K_M + full context166.9 GB- Can NVIDIA GeForce RTX 5090 run Qwen3 VL 235B A22B Thinking?
No — Qwen3 VL 235B A22B Thinking requires at least 91.8 GB at IQ3_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for Qwen3 VL 235B A22B Thinking?
For Qwen3 VL 235B A22B Thinking, Q4_K_M (141.9 GB) offers the best balance of quality and VRAM usage. Q5_K_S (162.5 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XXS at 91.8 GB.
VRAM requirement by quantization
IQ3_XXS91.8 GBQ4_0118.3 GBQ4_K_S133.1 GBQ4_K_M ★141.9 GBQ5_K_M168.4 GBBF16471.8 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 VL 235B A22B Thinking on a Mac?
Qwen3 VL 235B A22B Thinking requires at least 91.8 GB at IQ3_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Qwen3 VL 235B A22B Thinking locally?
Yes — Qwen3 VL 235B A22B Thinking can run locally on consumer hardware. At Q4_K_M quantization it needs 141.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 VL 235B A22B Thinking?
At Q4_K_M, Qwen3 VL 235B A22B Thinking can reach ~46 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 141.9 × 0.65 = ~128 tok/s
Estimated speed at Q4_K_M (141.9 GB)
~128 tok/s~128 tok/s~106 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3 VL 235B A22B Thinking?
At Q4_K_M, the download is about 141.40 GB. The full-precision BF16 version is 471.34 GB. The smallest option (IQ3_XXS) is 91.32 GB.
- Which GPUs can run Qwen3 VL 235B A22B Thinking?
No single consumer GPU has enough VRAM to run Qwen3 VL 235B A22B Thinking at Q4_K_M (141.9 GB). Multi-GPU or professional hardware is required.
- Which devices can run Qwen3 VL 235B A22B Thinking?
6 devices with unified memory can run Qwen3 VL 235B A22B Thinking at Q4_K_M (141.9 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.