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

Name: AI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with Gpus, Cuda, and Pytorch
Brand: Atlantic Books
SKU: 9798341627789
Price: 7013.0 INR
Availability: InStock

by Chris Fregly

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ISBN13: 9798341627789
Binding: Paperback
Subject: N/A
Publisher: O'Reilly Media
Publisher Imprint: O'Reilly Media
Publication Date: December 16, 2025
Pages: 1058
Original Price: GBP 79.99
Language: English
Edition: N/A
Item Weight: 1652 grams
BISAC Subject(s): Artificial Intelligence / Natural Language Processing

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

Fregly, Chris: - Chris Fregly is a performance engineer and AI product leader who has driven innovations at Netflix, Databricks, Amazon Web Services (AWS), and multiple startups. He has led performance-focused engineering teams that built AI/ML products, scaled go-to-market initiatives, and reduced cost for large-scale generative-AI and analytics workloads. Chris is coauthor of the Oâ€(TM)Reilly books Data Science on AWS and Generative AI on AWS, and creator of the Oâ€(TM)Reilly course "High-Performance AI in Production with NVIDIA GPUs." His work spans kernel-level tuning, compiler-driven acceleration, distributed training, and high-throughput inference. Chris is the organizer of the global AI Performance Engineering meetup with over 100,000 members worldwide.

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