May 2026
Intermediate
374 pages
5h 3m
Chinese
Overview
本作品已使用人工智能进行翻译。欢迎您提供反馈和意见：translation-feedback@oreilly.com
大型语言模型（LLM）是现代人工智能的推理引擎。如今，一个重要的拐点已经到来：随着全球竞相大规模部署人工智能，模型推理已成为人工智能堆栈的核心。欢迎来到推理时代。
然而，如果不进行适当的优化，LLMs 的服务可能既昂贵又缓慢。LLM 服务与优化实践》（Hands-On LLM Serving and Optimization）是一本全面介绍大规模部署和优化 LLM 复杂性的指南。
在这本注重工程实践的书中，作者 Chi Wang 和 Peiheng Hu 结合了实用的示例、代码和策略，以构建稳健、高性能和低成本的人工智能令牌工厂。无论您是在构建 LLM 推理基础架构，还是在构建消耗 LLM 推理基础架构的应用程序，在人工智能改变我们的工作和构建方式之际，深入了解 LLM 服务将使您成为一名更高效、为未来做好准备的工程师。
- 通过核心概念、设计范例和行业最佳实践了解模型服务的基础
- 了解大规模托管 LLM 的常见挑战
- 平衡延迟和吞吐量，满足人工智能应用的需求和业务要求
- 利用实用的代码支持技术，经济高效地托管 LLMs
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