Instructions to use unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF:UD-Q4_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF:UD-Q4_K_XL
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./llama-cli -hf unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF:UD-Q4_K_XL
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF:UD-Q4_K_XL
Use Docker
docker model run hf.co/unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF:UD-Q4_K_XL
- LM Studio
- Jan
- vLLM
How to use unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF:UD-Q4_K_XL
- Ollama
How to use unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF with Ollama:
ollama run hf.co/unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF:UD-Q4_K_XL
- Unsloth Desktop
- Pi
How to use unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF:UD-Q4_K_XL
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF:UD-Q4_K_XL" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF with Docker Model Runner:
docker model run hf.co/unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF:UD-Q4_K_XL
- Lemonade
How to use unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF:UD-Q4_K_XL
Run and chat with the model
lemonade run user.Qwen3-VL-30B-A3B-Thinking-GGUF-UD-Q4_K_XL
List all available models
lemonade list
- Hermes Agent
How to use unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF:UD-Q4_K_XL
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF:UD-Q4_K_XL
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF:UD-Q4_K_XL
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF:UD-Q4_K_XL" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
k6 k xl ud is bugged
k6 k xl ud is bugged. all the rest is fine
That was fast!!! Outstanding :)
Its free brother. I am not seeing Vision models able to run at home from USA or Europe .... At least that good
Is this model really that bad at detecting multiple celebs in one image? Can someone confirm? Gemini 2.5 flash easily gets it right.
I've found the entire Qwen3-VL series to be the most accurate vision models I've ever used, even beating Gemini, ChatGPT, etc. I ran your image through Qwen-3-VL-30B-A3B-Instruct. I know this is regarding the thinking variant, but I can't load it atm due to other users currently using the Instruct variant on my server. It did really well, only a couple mistakes - completely reasonable for a model this size imo. Both the Instruct & Thinking versions of VL-30B-A3B have been doing extremely good for multimodal use in my experience. (I'm using the FP16 mmproj instead of FP32, btw)
Maybe try implementing the Qwen-Agent "Zoom in tool" that allows the model to zoom in on different aspects of the image for better overal analysis.
I wouldn't call it "couple mistakes". It's less than 50% to me. While gemini flash 100% correct

