Introduction to Ray Serve LLM: Foundations of Large Language Model Serving

Introduction to Ray Serve LLM: Foundations of Large Language Model Serving#

💻 Launch Locally: You can run this notebook locally, but performance will be reduced.

🚀 Launch on Cloud: A Ray Cluster with GPUs (Click here to easily start a Ray cluster on Anyscale) is recommended to run this notebook.

This module provides a comprehensive introduction to serving Large Language Models (LLMs) with Ray Serve LLM. We’ll explore the fundamentals of LLM serving, understand the challenges, and learn how Ray Serve LLM provides production-grade solutions for deploying LLMs at scale.

Here is the roadmap for this module:

What is LLM Serving?
Key Concepts and Optimizations
Challenges in LLM Serving
Ray Serve LLM Architecture
Getting Started with Ray Serve LLM
Key Takeaways