Qwen3 VL 4B Thinking — Hardware Requirements & GPU Compatibility
Vision
Qwen3 VL 4B Thinking is Alibaba's 4.4-billion-parameter vision-language model in the Qwen3-VL lineup, the reasoning-enhanced Thinking edition that works through images and text before answering. The card highlights visual agent abilities for operating PC and mobile GUIs, visual coding from images and video, stronger spatial perception, and OCR support for 32 languages. At this size, it runs comfortably on a single modest consumer GPU once quantized. The model supports a 262,144 token context window, which the card describes as native 256K and expandable to 1M. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use. Published in October 2025, it pairs with an Instruct variant of the same size and adds Interleaved-MRoPE, DeepStack and text-timestamp alignment for video understanding.
54.3K downloads
120 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 3
- Parameters
- 4.4B
- Architecture
- Qwen3VLForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-10-11
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen3 VL 4B Thinking Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 2.4 GB | 26.4 GB | 1.89 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 2.4 GB | 26.4 GB | 1.94 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 2.6 GB | 26.6 GB | 2.16 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 2.7 GB | 26.7 GB | 2.22 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 3.1 GB | 27.1 GB | 2.66 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 3.6 GB | 27.6 GB | 3.16 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 4.2 GB | 28.1 GB | 3.66 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 4.9 GB | 28.9 GB | 4.44 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3 VL 4B Thinking?
Q4_K_M · 3.1 GBQwen3 VL 4B Thinking (Q4_K_M) requires 3.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 5+ GB is recommended. Using the full 262K context window can add up to 24.0 GB, bringing total usage to 27.1 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Qwen3 VL 4B Thinking?
Q4_K_M · 3.1 GB59 devices with unified memory can run Qwen3 VL 4B Thinking, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Qwen3 VL 4B Thinking
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Qwen3 VL 4B Thinking need?
Qwen3 VL 4B Thinking requires 3.1 GB of VRAM at Q4_K_M, or 9.4 GB at BF16. Full 262K context adds up to 24.0 GB (27.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 4.4B × 4.8 bits ÷ 8 = 2.7 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 24.4 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M3.1 GBQ4_K_M + full context27.1 GB- What's the best quantization for Qwen3 VL 4B Thinking?
For Qwen3 VL 4B Thinking, Q4_K_M (3.1 GB) offers the best balance of quality and VRAM usage. Q4_K_L (3.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 1.7 GB.
VRAM requirement by quantization
IQ2_XXS1.7 GBIQ3_XS2.3 GBQ3_K_L2.8 GBQ4_K_M ★3.1 GBQ4_K_L3.2 GBBF169.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 VL 4B Thinking on a Mac?
Qwen3 VL 4B Thinking requires at least 1.7 GB at IQ2_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 4B Thinking locally?
Yes — Qwen3 VL 4B Thinking can run locally on consumer hardware. At Q4_K_M quantization it needs 3.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 VL 4B Thinking?
At Q4_K_M, Qwen3 VL 4B Thinking can reach ~1524 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~208 tok/s. 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 ÷ 3.1 × 0.65 = ~1651 tok/s
Estimated speed at Q4_K_M (3.1 GB)
~1651 tok/s~208 tok/s~1651 tok/s~1524 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 4B Thinking?
At Q4_K_M, the download is about 2.66 GB. The full-precision BF16 version is 8.88 GB. The smallest option (IQ2_XXS) is 1.22 GB.
- Which GPUs can run Qwen3 VL 4B Thinking?
52 consumer GPUs can run Qwen3 VL 4B Thinking at Q4_K_M (3.1 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen3 VL 4B Thinking?
59 devices with unified memory can run Qwen3 VL 4B Thinking at Q4_K_M (3.1 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.