Running Your Own LLM Locally with vLLM: A Practical Introduction

As AI adoption grows, more developers are asking an interesting question:

What if I could run an LLM on my own infrastructure?

In a recent tutorial, IBM's Cedric Clyburn walks through how to self-host and serve open-weight language models using vLLM, one of the most popular high-performance inference engines for LLM deployment.

The session covers much more than simply loading a model. It explores the practical considerations that matter when running AI locally, including GPU memory requirements, quantization strategies, inference performance, and API-based model serving.

Some of the topics covered include:

• What vLLM is and why it is becoming popular for AI inference
• Continuous Batching and Paged Attention concepts
• Running LLMs directly from Python
• Serving models through API endpoints
• Offline inference workflows
• Deploying and evaluating AI models in production environments

What I particularly like about this type of content is that it moves beyond prompting and into AI infrastructure. Understanding how models are deployed, optimized, and served gives developers a much deeper appreciation of what's happening behind the scenes.

Whether you're experimenting with local AI, evaluating self-hosted deployments, or trying to understand the trade-offs between cloud and on-premise inference, vLLM provides a practical starting point.

And while exploring self-hosted AI deployments, tools like IBM Bob can help accelerate development workflows, allowing you to focus more on building and integrating AI-powered applications.

🎥 Watch the tutorial: vLLM Self-Hosting Walkthrough

💻 Explore the accompanying code: Project Repository

🚀 Try IBM Bob: Start Free Trial