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 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 for setup instructions.

Docker

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

• qwenllm/qwenvl on Docker Hub
• Pre-configured environments
• Easy to launch demos

See the Docker deployment guide 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 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
• SGLang Deployment
• Docker Deployment
• API Service