Qwen3 VL 235B A22B Instruct — Hardware Requirements & GPU Compatibility
Vision
Qwen3 VL 235B A22B Instruct is Alibaba's large vision-language model from the Qwen3 series, with 235.7 billion total parameters arranged as a Mixture-of-Experts network that activates about 22.8 billion per token, keeping generation relatively fast even though the full weight set must still fit in memory. It combines text generation with image and video understanding, including document parsing, OCR, and agentic tasks such as operating on-screen GUI elements. At this scale, local inference needs multiple high-VRAM GPUs or a large unified-memory machine, so most people use a hosted endpoint instead. It supports a 262K token context window for long documents, multi-image input, and video. It is released under the Apache 2.0 license, and was published in September 2025, pairing Qwen3's vision-language stack with agentic GUI control and video grounding.
330.7K downloads
419 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 Instruct 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 Instruct?
Q4_K_M · 141.9 GBQwen3 VL 235B A22B Instruct (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 Instruct?
Q4_K_M · 141.9 GB6 devices with unified memory can run Qwen3 VL 235B A22B Instruct, 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 Instruct
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 Instruct need?
Qwen3 VL 235B A22B Instruct 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 Instruct?
No — Qwen3 VL 235B A22B Instruct 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 Instruct?
For Qwen3 VL 235B A22B Instruct, 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 Instruct on a Mac?
Qwen3 VL 235B A22B Instruct 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 Instruct locally?
Yes — Qwen3 VL 235B A22B Instruct 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 Instruct?
At Q4_K_M, Qwen3 VL 235B A22B Instruct 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 Instruct?
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 Instruct?
No single consumer GPU has enough VRAM to run Qwen3 VL 235B A22B Instruct at Q4_K_M (141.9 GB). Multi-GPU or professional hardware is required.
- Which devices can run Qwen3 VL 235B A22B Instruct?
6 devices with unified memory can run Qwen3 VL 235B A22B Instruct 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.