<p>Run a ready-to-use Docker image with vLLM and JupyterLab IDE on top of your ROCm-enabled system! All software is preinstalled under containers, so you can jump straight into your workflow—get started with either SSH or Jupyter. <a href="https://docs.vllm.ai/en/latest/" rel="noopener" target="_blank">vLLM</a> accelerates large-language-model inference and supports a <a href="https://docs.vllm.ai/en/v0.17.1/models/supported_models/" rel="noopener" target="_blank">variety of models</a>. This 1-click setup also bundles five hands-on tutorials to guide you through vLLM and how to use it:</p>
<pre><code>- inference_server_vllm.ipynb
- unsloth_train_reasoning_model.ipynb
- chatbot2rapbot_vllm.ipynb
- build_ocr_vllm.ipynb
- build_airbnb_agent_mcp.ipynb</code></pre>
<p>All tutorials are available in Jupyter by default. If you don&#39;t have a HuggingFace token, we recommend following the <code>build_airbnb_agent_mcp.ipynb</code> tutorial, as it doesn&#39;t require a token.</p>

ROCm (host system)

ROCm (in Docker image)

JupyterLab (in Docker image)

vLLM (in Docker image)

vLLM 0.17.1, ROCm 7.2.0. Optimized for LLM inference and serving.

AMD Developer Cloud

vLLM 0.17.1

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Software Included

Getting started after deploying vLLM 0.17.1