Serving Large Language Models: Efficient Inference and KV Caching

Deploying large language models to production can be incredibly slow and expensive if you do not optimize how they generate text. To build responsive applications for multiple users, you must understand the mechanics of model inference and serving. This text-based course guides you from the absolute basics of token prediction to deploying optimized, high-performance LLM APIs. You will learn how to reduce latency and maximize throughput using modern serving strategies. What you'll learn: - Understand how language models predict tokens and why generation is computationally heavy - Implement KV caching to eliminate redundant calculations and speed up text generation - Apply continuous batching and queuing techniques to handle multiple concurrent users - Explore modern quantization methods to reduce the memory footprint of your models - Configure asynchronous request handling to build highly responsive LLM application backends You will start by exploring the foundational architecture of LLM generation and key terminology before diving into hands-on code examples for KV caching, batching, and async serving. This course is designed for software developers and aspiring AI engineers who are new to model deployment and want to learn efficient serving techniques from scratch. Start reading today to build faster, more cost-effective AI applications.