Hands - On LLM Serving and Optimization Hosting LLMs at Scale (True PDF)
Category: eBooks
- views: 197
- date: 29 May 2026
- posted by: BeMyLove
HandOn LLM Serving and Optimization Hosting LLMs at Scale (True PDF) | 11.84 MB
Title: Hands-On LLM Serving and Optimization
Author: Chi Wang and Peiheng Hu
Category: Nonfiction, Computers, Advanced Computing, Natural Language Processing, Programming, Software Development, General Computing
Language: English | 374 Pages | ISBN: 9798341621466
Description:
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.
DOWNLOAD:
https://rapidgator.net/file/af19422fc8474289e9635c7208deab96/Hands-On_LLM_Serving_and_Optimization__Hosting_LLMs_at_Scale_True_PDF.rar
https://nitroflare.com/view/C76CBE685BF616C/Hands-On_LLM_Serving_and_Optimization__Hosting_LLMs_at_Scale_True_PDF.rar
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 practices
- Understand the common challenges of hosting LLMs at scale
- Balance latency and throughput to meet the demands of AI applications and business requirements
- Host LLMs cost-effectively with practical, code-backed techniques
DOWNLOAD:
https://rapidgator.net/file/af19422fc8474289e9635c7208deab96/Hands-On_LLM_Serving_and_Optimization__Hosting_LLMs_at_Scale_True_PDF.rar
https://nitroflare.com/view/C76CBE685BF616C/Hands-On_LLM_Serving_and_Optimization__Hosting_LLMs_at_Scale_True_PDF.rar
We need your support!
Make a donation to help us stay online
Bitcoin (BTC)
bc1q08g9d22cxkawsjlf8etuek2pc9n2a3hs4cdrld
Bitcoin Cash (BCH)
qqvwexzhvgauxq2apgc4j0ewvcak6hh6lsnzmvtkem
Ethereum (ETH)
0xb55513D2c91A6e3c497621644ec99e206CDaf239
Litecoin (LTC)
ltc1qt6g2trfv9tjs4qj68sqc4uf0ukvc9jpnsyt59u
USDT (ERC20)
0xb55513D2c91A6e3c497621644ec99e206CDaf239
USDT (TRC20)
TYdPNrz7v1P9riWBWZ317oBgJueheGjATm
Information |
|||
 |
Users of GUESTS are not allowed to comment this publication. | ||