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Learn how to deploy a large language model using Runpod’s vLLM workers. By the end of this guide, you’ll have a fully functional Serverless endpoint that can handle LLM inference requests.

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What you’ll learn

In this tutorial, you’ll learn how to:
  • Configure and deploy a vLLM worker using Runpod Serverless.
  • Select the appropriate hardware and scaling settings for your model.
  • Set up environment variables to customize your deployment.
  • Test your endpoint using the Runpod API.
  • Troubleshoot common issues that might arise during deployment.

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Requirements

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Step 1: Choose your model

First, decide which LLM you want to deploy. The vLLM worker supports most models on Hugging Face, including:
  • Llama 3 (e.g., meta-llama/Llama-3.2-3B-Instruct).
  • Mistral (e.g., mistralai/Ministral-8B-Instruct-2410).
  • Qwen3 (e.g., Qwen/Qwen3-8B).
  • OpenChat (e.g., openchat/openchat-3.5-0106).
  • Gemma (e.g., google/gemma-3-1b-it).
  • DeepSeek-R1 (e.g., deepseek-ai/DeepSeek-R1-Distill-Qwen-7B).
  • Phi-4 (e.g., microsoft/Phi-4-mini-instruct).
For this walkthrough, we’ll use openchat/openchat-3.5-0106, but you can substitute this with any compatible model.

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Step 2: Deploy using the Runpod console

The easiest way to deploy a vLLM worker is through Runpod’s Ready-to-Deploy Repos:
  1. Find the vLLM repo in the Runpod Hub.
  2. Click Deploy, using the latest vLLM worker version.
  3. In the Model (optional) field, end the model name: openchat/openchat-3.5-0106.
  4. Click Advanced to expand the vLLM settings.
  5. Set Max Model Length to 8192 (or an appropriate context length for your model).
  6. Leave other settings at their defaults unless you have specific requirements, then click Next.
  7. Click Create Endpoint
Your endpoint will now begin initializing. This may take several minutes while Runpod provisions resources and downloads the selected model.
For more details on how to optimize your endpoint, see Endpoint configurations.

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Step 3: Understand your endpoint

While your endpoint is initializing, let’s understand what’s happening and what you’ll be able to do with it. Runpod is creating a Serverless endpoint with your specified configuration, and the vLLM worker image is being deployed using your chosen model. Once deployment is complete, make a note of your Endpoint ID, as you’ll need this to make API requests.

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Step 4: Send a test request

To test your worker, click the Requests tab in the endpoint detail page:
On the left you should see the default test request: 
{
    "input": {
        "prompt": "Hello World"
    }
}
Leave the default input as is and click Run. The system will take a few minutes to initialize your workers. When the workers finish processing your request, you should see output on the right side of the page similar to this: 
{
  "delayTime": 638,
  "executionTime": 3344,
  "id": "f0706ead-c5ec-4689-937c-e21d5fbbca47-u1",
  "output": [
    {
      "choices": [
        {
          "tokens": ["CHAT_RESPONSE"]
        }
      ],
      "usage": {
        "input": 3,
        "output": 100
      }
    }
  ],
  "status": "COMPLETED",
  "workerId": "0e7o8fgmm9xgty"
}

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Step 5: Customize your deployment with environment variables (optional)

If you need to customize your model deployment, you can edit your endpoint settings to add environment variables. Here are some useful environment variables you might want to set:
  • MAX_MODEL_LEN: Maximum context length (e.g., 16384).
  • DTYPE: Data type for model weights (float16, bfloat16, or float32).
  • GPU_MEMORY_UTILIZATION: Controls VRAM usage (e.g., 0.95 for 95%).
  • CUSTOM_CHAT_TEMPLATE: For models that need a custom chat template.
  • OPENAI_SERVED_MODEL_NAME_OVERRIDE: Change the model name to use in OpenAI requests.
To add or modify environment variables:
  1. Go to your endpoint details page.
  2. Select Manage, then select Edit Endpoint.
  3. Expand the Public Environment Variables section.
  4. Add or edit your desired variables.
  5. Click Save Endpoint.
You can find a full list of available environment variables in the environment variables documentation. You may also wish to adjust the input parameters for your request. For example, use the max_tokens parameter to increase the maximum number of tokens generated per response. To learn more, see Send vLLM requests.

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Troubleshooting

If you encounter issues with your deployment:
  • Worker fails to initialize: Check that your model is compatible with vLLM and your GPU has enough VRAM.
  • Slow response times: Consider using a more powerful GPU or optimizing your request parameters.
  • Out of memory errors: Try increasing GPU size or reducing MAX_MODEL_LEN.
  • API errors: Verify your endpoint ID and API key are correct.

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Next steps

Congratulations! You’ve successfully deployed a vLLM worker on Runpod Serverless. You now have a powerful, scalable LLM inference API that’s compatible with both the OpenAI client and Runpod’s native API. Next you can try:
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