#model-serving

#model-serving

To learn how to build and deploy cutting-edge multimodal LLMs like LLaVA using the high-performance vLLM serving framework, just keep reading. Large Language Models (LLMs) have revolutionized the way we interact with machines - from writing assistance to reasoning engines. But until recently, they've largely been stuck in the world of text. Humans aren't wired that way. We make sense of the world using multiple modalities - vision, language, audio, and more - in a seamless, unified way.

In this tutorial, you'll learn how to set up the vLLM inference engine to serve powerful open-source multimodal models (e.g., LLaVA) - all without needing to clone any repositories. We'll install vLLM, configure your environment, and demonstrate two core workflows: offline inference and OpenAI-compatible API testing. By the end of this lesson, you'll have a blazing-fast, production-ready backend that can easily integrate with frontend tools such as Streamlit or your custom applications.