Hands-On LLM Serving and Optimization: Hosting LLMs at Scale

di Chi Wang (Autore)

Peiheng Hu (Autore)

O'Reilly Media, 2026

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

• Autore: Chi Wang, Peiheng Hu
• Editore: O'Reilly Media
• Anno: 2026
• Rilegatura: Paperback / softback
• Pagine: 300 p.
• Dimensioni: 232 x 178 mm
• EAN: 9798341621497

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