AI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with GPUs, CUDA, and PyTorch - Marjane Mall - Image 1
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AI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with GPUs, CUDA, and PyTorch

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Marque : GENERIC
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Elevate your AI system performance capabilities with this definitive guide to maximizing efficiency across every layer of your AI infrastructure. In today's era of ever-growing generative models, AI Systems Performance Engineering provides engineers, researchers, and developers with a hands-on set of actionable optimization strategies. Learn to co-...

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Description produit

Marque
GENERIC
Titre
AI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with GPUs, CUDA, and PyTorch
Éditeur
O'Reilly Media
Type de produit
paperback
Présentation du livre
paperback
Date de sortie
12/16/2025 12:00:00 AM
Langue d'origine
English
ISBN
1823301258
Dimensions
7 x 2 x 9.19 inches
Nombre de pages
1060 pages
Langue
English
Résumé
Elevate your AI system performance capabilities with this definitive guide to maximizing efficiency across every layer of your AI infrastructure. In today's era of ever-growing generative models, AI Systems Performance Engineering provides engineers, researchers, and developers with a hands-on set of actionable optimization strategies. Learn to co-optimize hardware, software, and algorithms to build resilient, scalable, and cost-effective AI systems that excel in both training and inference. Authored by Chris Fregly, a performance-focused engineering and product leader, this resource transforms complex AI 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. The book ends with a 175+-item checklist of proven, ready-to-use optimizations.Codesign and optimize hardware, software, and algorithms to achieve maximum throughput and cost savingsImplement cutting-edge inference strategies that reduce latency and boost throughput in real-world settingsUtilize industry-leading scalability tools and frameworksProfile, diagnose, and eliminate performance bottlenecks across complex AI pipelinesIntegrate full stack optimization techniques for robust, reliable AI system performance Read more
Auteur
Chris Fregly
Date de parution
12/16/2025 12:00:00 AM