wang peiheng (11 risultati)

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Paperback. Condizione: New.

paperback. Condizione: New.

Paperback. Condizione: New. 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 scenariosUnderstand the common challenges of hosting LLMs at scale while minimizing costsPick up practical techniques for optimizing LLM serving performanceBuild a model-serving system that meets specific business requirementsImprove LLM serving throughput and reduce latencyHost LLMs in a cost-effective manner, balancing performance and resource efficiency.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: New. 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 scenariosUnderstand the common challenges of hosting LLMs at scale while minimizing costsPick up practical techniques for optimizing LLM serving performanceBuild a model-serving system that meets specific business requirementsImprove LLM serving throughput and reduce latencyHost LLMs in a cost-effective manner, balancing performance and resource efficiency.

Condizione: New.

Paperback. Condizione: New. 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 scenariosUnderstand the common challenges of hosting LLMs at scale while minimizing costsPick up practical techniques for optimizing LLM serving performanceBuild a model-serving system that meets specific business requirementsImprove LLM serving throughput and reduce latencyHost LLMs in a cost-effective manner, balancing performance and resource efficiency.