Hands-on LLM serving and optimization / by Chi Wang and Peiheng Hu.

Format:

Book

Author/Creator:

Wang, Chi, author.

Hu, Peiheng, author.

Language:

English

Subjects (All):

Natural language generation (Computer science).

Artificial intelligence.

Physical Description:

1 online resource (314 pages)

Edition:

[First edition].

Place of Publication:

[Sebastopol, California] : O'Reilly Media, Inc., [2025]

Summary:

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.

Notes:

OCLC-licensed vendor bibliographic record.

OCLC:

1572438404