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Hands-On LLM Serving and Optimization: Hosting LLMs at Scale
Hands-On LLM Serving and Optimization is your comprehensive guide to deploying and optimizing LLMs at scale.
Hands-On LLM Serving and Optimization: Hosting LLMs at Scale
商品#: 230128021

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

商品#: 230128021

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Hands-On LLM Serving and Optimization is your comprehensive guide to deploying and optimizing LLMs at scale.
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What Stands Out

Scalability
Easily host and serve large language models at scale, catering to growing user demands without compromising performance or reliability.
Optimization Techniques
Incorporates advanced optimization strategies to enhance LLM performance, reducing latency and improving response times for users.
User-Centric Design
Designed for developers and businesses, ensuring a seamless integration experience with user-friendly interfaces and robust documentation.

產品詳情

Shop Hands-On LLM Serving and Optimization: Hosting LLMs at Scale online at a best price in Taiwan. B0G48JRRMF
  • Large language models (LLMs) are the reasoning engines of modern AI. Today, a major inflection point has arrived: as the world races to deploy AI at scale, model inference has moved to the center of the stack. Welcome to the inference era.Without proper optimization, however, LLMs can be expensive and slow to serve. Hands-On LLM Serving and Optimization is a comprehensive guide to the complexities of deploying and optimizing LLMs at scale.In this hands-on, engineering-focused book, authors Chi Wang and Peiheng Hu combine practical examples, code, and strategies for building robust, performant, and cost-efficient AI token factories. Whether you're building the LLM inference infrastructure or the applications that consume it, a deep understanding of LLM serving will make you a more effective, future-ready engineer as AI transforms how we work and build.Learn the foundations of model serving with core concepts, design paradigms, and industry best practicesUnderstand the common challenges of hosting LLMs at scaleBalance latency and throughput to meet the demands of AI applications and business requirementsHost LLMs cost-effectively with practical, code-backed techniques
Publisher O'Reilly Media
Publication date June 2, 2026
Edition 1st
Language English
Print length 371 pages
ISBN-13 979-8341621497
Item Weight 1.31 pounds (590 grams)
Dimensions 7 x 2 x 9.19 inches (17.8 x 5.1 x 23.3 cm)

Who Should Buy?

Suitable For
  • Data Scientists

    Ideal for data scientists looking to deploy large language models efficiently while optimizing resource usage and performance.

  • DevOps Engineers

    Tailored for DevOps engineers needing scalable solutions for hosting, managing, and maintaining large language models seamlessly.

  • AI Researchers

    Beneficial for AI researchers exploring practical implementations and optimizations of language models in real-world applications.

Not Suitable For
  • Small Startups

    Small startups with limited budgets may find the infrastructure and complexity too overwhelming for their needs.

產品描述

Dietary Supplement Disclaimer

Statements regarding dietary supplements have not been evaluated by the Food and Drug Administration and are not intended to diagnose, treat, cure, or prevent any disease or health condition.


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Natural Language Processing Editorial Review

Hands-On LLM Serving and Optimization: Hosting LLMs at Scale is a comprehensive guide published by O'Reilly Media, set to release on June 2, 2026. This 371-page book delves into strategies for efficiently hosting large language models (LLMs) at scale, combining technical insights with practical applications. With a focus on serving and optimization, it serves as an invaluable resource for developers and engineers looking to enhance their capabilities in managing LLMs. The book is written in English and offers a wealth of information for both newcomers and experienced practitioners in the field.

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優點

  • In-depth insights on hosting large language models
  • Practical strategies for optimization
  • Clearly structured for easy understanding
  • Technical guidance for developers and engineers
  • Comprehensive coverage of LLM serving techniques

缺點

  • The publication date is set for June 2026.

Product Price History

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