Qwen Alibaba Released 2025-10 visionmultimodaltools

Qwen 3 VL

The most powerful vision-language model in the Qwen3 family. Combines Qwen 3's reasoning with state-of-the-art visual understanding. 2B-235B sizes for any hardware.

Best for frontier-class vision-language with thinking
Sizes 2B · 4B · 8B · 30B · 32B · 235B
Context 128K
License Apache 2.0
Min VRAM (default size, Q4) 2 GB
Rec VRAM 8 GB

What is Qwen 3 VL?

Qwen 3 VL is Alibaba's open-weights language model with vision support. Released in 2025-10, it ships in 6 sizes (2B, 4B, 8B, 30B, 32B, 235B) and is licensed Apache 2.0. The most popular use case is frontier-class vision-language with thinking.

VRAM and hardware

The smallest 2B size needs at least 2 GB VRAM; the largest 235B needs around 2 GB.

Size Min VRAM (Q4_K_M) Recommended VRAM Tokens/sec on 3090
2B 2 GB 3 GB ~31 tok/s
4B 3 GB 5 GB ~28 tok/s
8B 6 GB 9 GB ~24 tok/s
30B 18 GB 27 GB ~18 tok/s
32B 20 GB 30 GB ~17 tok/s
235B 130 GB 195 GB ~11 tok/s

How to run Qwen 3 VL locally

Option 1: Ollama (simplest)

ollama pull qwen3-vl
ollama run qwen3-vl

Option 2: Mullama (production)

mullama pull qwen3-vl
mullama run qwen3-vl

Option 3: llama.cpp (CLI)

# Download a GGUF from Hugging Face (search "qwen3-vl gguf")
./llama-cli -m qwen3-vl.Q4_K_M.gguf -p "Hello, AI!"

Option 4: Python with Mullama or llama-cpp-python

from mullama import Model, Context
model = Model.load("qwen3-vl.Q4_K_M.gguf", n_gpu_layers=99)
ctx = Context(model, n_ctx=4096)
print(ctx.generate("Hello, AI!", 256))

What you can build with Qwen 3 VL

  • vision
  • document understanding
  • agentic
  • multimodal

Hardware it fits on

  • Apple Silicon (8GB): 2B in MLX
  • Apple Silicon (16GB): 4B in MLX
  • Apple Silicon (32GB): 8B in MLX or GGUF
  • Apple Silicon (64GB+): 235B in MLX
  • RTX 3090 (24GB): 8B at Q4_K_M
  • RTX 4090 (24GB): same as 3090, ~30% faster
  • RTX 5090 (32GB): 8B at Q8_0
  • 2× RTX 6000 Ada (96GB total): 235B at Q4_K_M

Related models in the Qwen family

  • Qwen 2.5 — 0.5B, 1.5B, 3B, 7B, 14B, 32B, 72B · strongest 7B-32B on consumer hardware
  • Qwen 2.5-Coder — 0.5B, 1.5B, 3B, 7B, 14B, 32B · best open-weight code model in 2024-2025
  • Qwen 3 — 0.6B, 1.7B, 4B, 8B, 14B, 30B, 32B, 235B · best all-round local LLM in 2025-2026
  • Qwen 3-Coder — 30B, 480B · long-context code agent workflows

Other models you might consider

  • Llama 3.1 (Meta) — 8B, general-purpose chat, agentic workflows, code
  • Llama 3.2 (Meta) — 1B, low-end hardware, mobile, edge deployment
  • Llama 3.2 Vision (Meta) — 11B, vision + chat on desktop GPUs
  • Llama 3.3 (Meta) — 70B, best-in-class 70B for desktops
  • Llama 4 (Meta) — Scout 17B (109B MoE), vision + long context (10M tokens)
  • DeepSeek R1 (DeepSeek) — 1.5B, reasoning, math, step-by-step problem solving