Large language models (LLMs) are rapidly becoming the backbone of AI-driven applications. Without proper optimization, however, LLMs can be expensive to run, slow to serve, and prone to performance bottlenecks. As the demand for real-time AI applications grows, along comes Hands-On Serving and Optimizing LLM Models, a comprehensive guide to the complexities of deploying and optimizing LLMs at scale.

In this hands-on book, authors Chi Wang and Peiheng Hu take a real-world approach backed by practical examples and code, and assemble essential strategies for designing robust infrastructures that are equal to the demands of modern AI applications. Whether you're building high-performance AI systems or looking to enhance your knowledge of LLM optimization, this indispensable book will serve as a pillar of your success.



Learn the key principles for designing a model-serving system tailored to popular business scenarios
Understand the common challenges of hosting LLMs at scale while minimizing costs
Pick up practical techniques for optimizing LLM serving performance
Build a model-serving system that meets specific business requirements
Improve LLM serving throughput and reduce latency
Host LLMs in a cost-effective manner, balancing performance and resource efficiency