> ## 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.

# Deployment Overview

> Learn about different deployment options for Qwen3-VL models

## Deployment Options

Qwen3-VL offers multiple deployment options to suit different use cases and infrastructure requirements. Choose the deployment method that best fits your needs:

### vLLM (Recommended)

We recommend using vLLM for fast Qwen3-VL deployment and inference. You need to install `vllm>=0.11.0` to enable Qwen3-VL support.

**Key Features:**

* Fast inference with optimized kernels
* Online serving with OpenAI-compatible API
* Offline inference support
* Efficient memory management
* Multi-GPU support with tensor parallelism

See the [vLLM deployment guide](https://mintlify.com/QwenLM/Qwen3-VL/deployment/vllm.md) for detailed instructions.

### SGLang

SGLang provides an alternative high-performance serving solution with:

* Fast inference engine
* OpenAI-compatible API
* Flexible configuration options

See the [SGLang deployment guide](https://mintlify.com/QwenLM/Qwen3-VL/deployment/sglang.md) for setup instructions.

### Docker

For simplified deployment, we provide pre-built Docker images with all dependencies configured:

* [qwenllm/qwenvl](https://hub.docker.com/r/qwenllm/qwenvl) on Docker Hub
* Pre-configured environments
* Easy to launch demos

See the [Docker deployment guide](https://mintlify.com/QwenLM/Qwen3-VL/deployment/docker.md) for usage instructions.

### DashScope API Service

For production use without managing infrastructure, you can use the DashScope API service:

* Fully managed service
* OpenAI-compatible client
* No infrastructure setup required
* Scalable and reliable

See the [API service guide](https://mintlify.com/QwenLM/Qwen3-VL/deployment/api-service.md) for integration details.

## Choosing a Deployment Method

| Method | Best For | Setup Complexity | Performance |
| - | - | - | - |
| vLLM | Production deployments, high throughput | Medium | Excellent |
| SGLang | Alternative to vLLM, flexible configs | Medium | Excellent |
| Docker | Quick start, demos, development | Low | Good |
| DashScope API | No infrastructure management | Very Low | Excellent |

## Next Steps

Explore the detailed deployment guides:

* [vLLM Deployment](https://mintlify.com/QwenLM/Qwen3-VL/deployment/vllm.md)
* [SGLang Deployment](https://mintlify.com/QwenLM/Qwen3-VL/deployment/sglang.md)
* [Docker Deployment](https://mintlify.com/QwenLM/Qwen3-VL/deployment/docker.md)
* [API Service](https://mintlify.com/QwenLM/Qwen3-VL/deployment/api-service.md)


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