> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/QwenLM/Qwen3-VL/llms.txt
> Use this file to discover all available pages before exploring further.

# Docker Deployment

> Deploy Qwen3-VL using pre-built Docker images

## Overview

To simplify the deployment process, we provide Docker images with pre-built environments. You only need to install the GPU driver and download model files to launch demos.

## Docker Image

Our official Docker images are available on Docker Hub:

* **Repository**: [qwenllm/qwenvl](https://hub.docker.com/r/qwenllm/qwenvl)
* **Tag**: `qwen3vl-cu128` (CUDA 12.8)

## Quick Start

Run the Docker container with GPU support:

```bash theme={null}
docker run --gpus all --ipc=host --network=host --rm --name qwen3vl -it qwenllm/qwenvl:qwen3vl-cu128 bash
```

### Command Breakdown

* `--gpus all`: Enable access to all available GPUs
* `--ipc=host`: Use host IPC namespace (required for shared memory)
* `--network=host`: Use host network stack
* `--rm`: Automatically remove the container when it exits
* `--name qwen3vl`: Assign a name to the container
* `-it`: Run in interactive mode with a terminal

## Running Web Demo

For a quick start with the web demo, use the provided script:

```bash theme={null}
cd docker && bash run_web_demo.sh -c /your/path/to/qwen3vl/weight --port 8881
```

### Parameters

* `-c`: Path to the Qwen3-VL model weights
* `--port`: Port number for the web interface (default: 8881)

## Using the Container

Once inside the container, you have access to:

* Pre-installed dependencies (transformers, vLLM, etc.)
* Python environment configured for Qwen3-VL
* All required CUDA libraries

### Example Usage

After entering the container, you can run inference scripts:

```bash theme={null}
# Inside the container
python your_inference_script.py
```

Or start a vLLM server:

```bash theme={null}
vllm serve Qwen/Qwen3-VL-235B-A22B-Instruct-FP8 \
  --tensor-parallel-size 8 \
  --mm-encoder-tp-mode data \
  --enable-expert-parallel \
  --async-scheduling \
  --media-io-kwargs '{"video": {"num_frames": -1}}' \
  --host 0.0.0.0 \
  --port 22002
```

## Mounting Local Directories

To access local model files or data, mount directories when running the container:

```bash theme={null}
docker run --gpus all --ipc=host --network=host \
  -v /local/path/to/models:/models \
  -v /local/path/to/data:/data \
  --rm --name qwen3vl -it qwenllm/qwenvl:qwen3vl-cu128 bash
```

## Prerequisites

### GPU Driver

Ensure you have the NVIDIA GPU driver installed on your host system:

* **Minimum version**: 525.60.13 or later
* **Recommended**: Latest stable driver for your GPU

### Docker and nvidia-docker

Install Docker and the NVIDIA Container Toolkit:

```bash theme={null}
# Install Docker (Ubuntu/Debian)
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh

# Install NVIDIA Container Toolkit
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | \
  sudo tee /etc/apt/sources.list.d/nvidia-docker.list

sudo apt-get update
sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker
```

## Troubleshooting

### GPU Not Available

If GPUs are not accessible inside the container:

1. Verify GPU driver installation:
   ```bash theme={null}
   nvidia-smi
   ```

2. Check Docker runtime:
   ```bash theme={null}
   docker run --gpus all nvidia/cuda:12.8.0-base-ubuntu22.04 nvidia-smi
   ```

### Out of Memory

If you encounter out-of-memory errors:

* Reduce batch size in your inference script
* Use quantized models (FP8, INT8, INT4)
* Increase `--ipc=host` shared memory allocation

## Next Steps

* Explore [vLLM deployment](https://mintlify.com/QwenLM/Qwen3-VL/deployment/vllm.md) for production use
* Try [SGLang deployment](https://mintlify.com/QwenLM/Qwen3-VL/deployment/sglang.md) as an alternative
* Learn about the [DashScope API service](https://mintlify.com/QwenLM/Qwen3-VL/deployment/api-service.md) for managed hosting


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