Inference Engineering

Optimize, accelerate, and serve LLMs
Chapters written by Guglielmo Iozzia, Pere Martra, Vivek Kalyanarangan, Harshwardhan Fartale
  • October 2026
  • ISBN 9781638358879
  • 187 pages

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Find out what’s slowing your LLM down—and what you can do about it. This eBook brings together five chapters from Manning books to help you make informed choices about inference, model efficiency, and production deployment. Explore ways to ease memory pressure, balance quantization savings with output quality, and reduce data movement with fused Triton kernels. Then learn how batching, streaming, and rate limiting help you handle requests in a running service. Whether you’re refining a prototype or preparing for deployment, you’ll gain practical techniques to try on your own workloads and a stronger foundation for further study of LLM optimization and GPU programming.

about the authors

Guglielmo Iozzia is a Director of AI and Applied Mathematics at Merck & Co. and a Distinguished Member of the American Society for Artificial Intelligence. He specializes in AI biomedical applications.

Pere Martra is an Applied AI Engineer, the creator of the Optipfair model efficiency library, an international AI speaker, and maintainer of widely-used LLM courses and popular open-source tools. He is the author of Large Language Models Projects.

Vivek Kalyanarangan is an AI/ML architect, researcher, and educator with over twelve years of experience designing and deploying large-scale machine learning systems.

Harshwardhan Fartale is a researcher and engineer based in Bangalore, where he builds machine learning systems for scientific and defense applications. He has delivered courses in generative AI, machine learning, and MLOps to audiences ranging from university students to national research bodies.
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