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How to run Qwen with vLLM

Q: How to run Qwen with vLLM

Use the vllm Python package to run the meta-qwen/Qwen-7B model locally by serving it with the vllm serve CLI command and then querying it via the openai SDK with base_url="http://localhost:8000/v1". This enables efficient, low-latency inference of Qwen with familiar OpenAI-compatible API calls.

Quick answer

Use the vllm Python package to run the meta-qwen/Qwen-7B model locally by serving it with the vllm serve CLI command and then querying it via the openai SDK with base_url="http://localhost:8000/v1". This enables efficient, low-latency inference of Qwen with familiar OpenAI-compatible API calls.

PREREQUISITES

Python 3.8+
pip install vllm openai
Qwen model weights downloaded or accessible
Basic knowledge of command line and Python

Setup

Install the vllm package and the openai Python SDK. Download the Qwen model weights or ensure you have access to meta-qwen/Qwen-7B locally. The vllm server will host the model for inference.

bash

pip install vllm openai

Step by step

Start the vllm server to serve the Qwen model, then run a Python script to query it using the OpenAI-compatible API.

python

# Step 1: Start the vLLM server with Qwen model
vllm serve meta-qwen/Qwen-7B --port 8000

# Step 2: Python script to query the running server
import os
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"], base_url="http://localhost:8000/v1")

response = client.chat.completions.create(
    model="meta-qwen/Qwen-7B",
    messages=[{"role": "user", "content": "Explain the benefits of using vLLM with Qwen."}]
)
print(response.choices[0].message.content)

output

The benefits of using vLLM with Qwen include efficient memory usage, fast batch inference, and compatibility with OpenAI API calls, enabling seamless integration into existing workflows.

Common variations

Use different Qwen variants by changing the model name in the vllm serve command and the Python client.
Enable streaming responses by setting stream=True in the chat.completions.create call.
Run asynchronous queries using Python asyncio with the OpenAI SDK.

python

import asyncio
import os
from openai import OpenAI

async def async_query():
    client = OpenAI(api_key=os.environ["OPENAI_API_KEY"], base_url="http://localhost:8000/v1")
    response = await client.chat.completions.acreate(
        model="meta-qwen/Qwen-7B",
        messages=[{"role": "user", "content": "What is vLLM?"}]
    )
    print(response.choices[0].message.content)

asyncio.run(async_query())

output

vLLM is a high-performance inference engine designed for large language models, providing efficient batching and low latency.

Troubleshooting

If the server fails to start, verify the model path and that you have sufficient GPU memory.
If you get connection errors, ensure the vllm serve process is running on port 8000 and accessible.
For model loading issues, confirm the Qwen model weights are correctly downloaded and compatible with vllm.

✅

Key Takeaways

Run Qwen locally with vLLM using the CLI vllm serve command.
Query the running server via OpenAI-compatible Python SDK with base_url="http://localhost:8000/v1".
Use async and streaming options for flexible inference patterns.
Ensure model weights and GPU resources are properly configured for smooth operation.

Verified 2026-04 · meta-qwen/Qwen-7B

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