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Home > Computing and Information Technology > Computer science > Artificial intelligence > Natural language and machine translation > AI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with Gpus, Cuda, and Pytorch
AI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with Gpus, Cuda, and Pytorch

AI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with Gpus, Cuda, and Pytorch


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About the Book

Elevate your AI system performance capabilities with this definitive guide to unlocking peak efficiency across every layer of your AI infrastructure. In today's era of ever-growing generative models, AI Systems Performance Engineering equips professionals with actionable strategies to co-optimize hardware, software, and algorithms for high-performance and cost-effective AI systems. Authored by Chris Fregly, a performance-focused engineering and product leader, this comprehensive resource transforms complex systems into streamlined, high-impact AI solutions. Inside, you'll discover step-by-step methodologies for fine-tuning GPU CUDA kernels, PyTorch-based algorithms, and multinode training and inference systems. You'll also master the art of scaling GPU clusters for high performance, distributed model training jobs, and inference servers. Codesign and optimize hardware, software, and algorithms to achieve maximum throughput and cost savings Implement cutting-edge inference strategies that reduce latency and boost throughput in real-world settings Utilize industry-leading scalability tools and frameworks Profile, diagnose, and eliminate performance bottlenecks across complex AI pipelines Integrate full stack optimization techniques for robust, reliable AI system performance Whether you're an engineer, researcher, or developer, AI Systems Performance Engineering gives you a holistic roadmap for building resilient, scalable, and cost-effective AI systems that excel in both training and inference.

About the Author :
Chris Fregly is a passionate performance engineer and AI product leader with a proven track record of driving innovation at leading tech companies like Netflix, Databricks, and Amazon Web Services (AWS). He's led performance-focused engineering teams that built advanced AI/ML products, scaled go-to-market initiatives, and reduced cost for large-scale generative AI and analytics workloads. He is also co-author of 2 O'Reilly books: Data Science on AWS and Generative AI on AWS - as well as the creator of the O'Reilly online course titled, "High Performance AI in Production with Nvidia GPUs".


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Product Details
  • ISBN-13: 9798341627789
  • Publisher: O'Reilly Media
  • Publisher Imprint: O'Reilly Media
  • Language: English
  • Returnable: Y
  • ISBN-10: 8341627787
  • Publisher Date: 23 Dec 2025
  • Binding: Paperback
  • No of Pages: 954
  • Sub Title: Optimizing Model Training and Inference Workloads with Gpus, Cuda, and Pytorch


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