Instructions to use Abiray/ComfyUI-Qwen3-VL-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 Abiray/ComfyUI-Qwen3-VL-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 Abiray/ComfyUI-Qwen3-VL-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Abiray/ComfyUI-Qwen3-VL-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Abiray/ComfyUI-Qwen3-VL-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Abiray/ComfyUI-Qwen3-VL-GGUF:Q4_K_M
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 Abiray/ComfyUI-Qwen3-VL-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Abiray/ComfyUI-Qwen3-VL-GGUF:Q4_K_M
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 Abiray/ComfyUI-Qwen3-VL-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Abiray/ComfyUI-Qwen3-VL-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Abiray/ComfyUI-Qwen3-VL-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Abiray/ComfyUI-Qwen3-VL-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Abiray/ComfyUI-Qwen3-VL-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": "Abiray/ComfyUI-Qwen3-VL-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/Abiray/ComfyUI-Qwen3-VL-GGUF:Q4_K_M
- Ollama
How to use Abiray/ComfyUI-Qwen3-VL-GGUF with Ollama:
ollama run hf.co/Abiray/ComfyUI-Qwen3-VL-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use Abiray/ComfyUI-Qwen3-VL-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Abiray/ComfyUI-Qwen3-VL-GGUF:Q4_K_M
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": "Abiray/ComfyUI-Qwen3-VL-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Abiray/ComfyUI-Qwen3-VL-GGUF with Docker Model Runner:
docker model run hf.co/Abiray/ComfyUI-Qwen3-VL-GGUF:Q4_K_M
- Lemonade
How to use Abiray/ComfyUI-Qwen3-VL-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Abiray/ComfyUI-Qwen3-VL-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.ComfyUI-Qwen3-VL-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Abiray/ComfyUI-Qwen3-VL-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 Abiray/ComfyUI-Qwen3-VL-GGUF:Q4_K_M
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 Abiray/ComfyUI-Qwen3-VL-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Abiray/ComfyUI-Qwen3-VL-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Abiray/ComfyUI-Qwen3-VL-GGUF:Q4_K_M
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 "Abiray/ComfyUI-Qwen3-VL-GGUF:Q4_K_M" \ --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"
ComfyUI Qwen3-VL GGUF (8B & 4B)
GGUF quantizations and unquantized multimodal projectors (mmproj) extracted from Comfy-Org's Qwen3-VL repackaged weights.
These artifacts enable lightweight visual language reasoning across ComfyUI-GGUF, llama.cpp, llama-server, and Ollama on consumer hardware and CPU runtimes.
Model Overview
- Original Weights Source: Comfy-Org/Qwen3-VL
- Underlying Architecture: Alibaba Cloud Qwen3-VL (
Qwen3VLForConditionalGeneration) - Projector Format: Standalone F16 GGUF (
mmproj) preserving full vision patch fidelity - Quantization Engine:
llama.cpp(b11209) k-quants
Available Files & Specifications
8B Series
| File Name | Precision | File Size | Description |
|---|---|---|---|
mmproj-qwen3vl-8b-f16.gguf |
F16 | 1.08 GB | Multimodal vision projector (required for image reasoning) |
qwen3vl-8b-Q4_K_M.gguf |
Q4_K_M | 4.68 GB | Optimal balance of generation speed, memory, and accuracy |
qwen3vl-8b-Q5_K_M.gguf |
Q5_K_M | 5.45 GB | Balanced higher-precision text backbone |
qwen3vl-8b-Q6_K.gguf |
Q6_K | 6.26 GB | Near-lossless language reasoning and instruction following |
4B Series
| File Name | Precision | File Size | Description |
|---|---|---|---|
mmproj-qwen3vl-4b-f16.gguf |
F16 | 0.78 GB | 4B Multimodal vision projector |
qwen3vl-4b-Q4_K_M.gguf |
Q4_K_M | 2.33 GB | Ultra-lightweight edge quant for low-memory environments |
qwen3vl-4b-Q5_K_M.gguf |
Q5_K_M | 2.69 GB | Precision 4B variant |
Note on Projectors (
mmproj): The vision projector maps image patches into the language model's latent embedding space. It is kept in unquantized F16 to prevent color shifts, bounding box degradation, or spatial grounding errors.
Quickstart & Usage
1. ComfyUI Setup (via ComfyUI-GGUF / ComfyUI-QwenVL)
- Place your desired language GGUF (e.g.,
qwen3vl-8b-Q4_K_M.gguf) into:ComfyUI/models/unet/ # or ComfyUI/models/text_encoders/ - Place the matching projector (e.g.,
mmproj-qwen3vl-8b-f16.gguf) into:ComfyUI/models/clip_vision/ - Load the model using standard
UnetLoaderGGUForDualCLIPLoaderGGUFnodes in your workflow.
Acknowledgements
- Alibaba Cloud Qwen Team for the foundational Qwen3-VL models.
- Comfy-Org for packaging the unified BF16 safetensors weights.
- Georgi Gerganov and the GGML/llama.cpp contributors for GGUF architecture and quantization tooling.
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