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Artículo: AMZ-B0G5CF4P8P
Large-Scale AI Engineering: Design, Train, and Optimize Foundation Models on NVIDIA GPU Clusters
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Kindle
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0.84 kg
0.84 kg
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Nuevo
Nuevo
Producto de
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Build, Scale, and Master AI Systems Powered by Modern GPU SupercomputingIf you’re ready to understand how real AI infrastructure works, from GPU memory hierarchies to NVLink fabrics, Transformer Engine optimizations, multi-node orchestration, and rack-scale networking, this book gives you the practical blueprint you’ve been searching for. What This Book Allows You to DoThis book empowers you to design, optimize, and scale large-scale AI systems using modern GPU architectures such as NVIDIA’s H100/H200, multi-rail InfiniBand, NVSwitch fabrics, and high-efficiency FP8 computation. It takes you from fundamentals to advanced, production-grade engineering with clarity and depth. About the TechnologyModern AI requires more than large models, it demands high-bandwidth computation, distributed training topologies, NUMA-aware networking, and GPU interconnect fabrics that can move data at terabytes per second. This book breaks down the full stack:GPU architecture, HBM, memory hierarchies, and FP8 acceleration3D parallelism, tensor pipelines, and distributed model shardingNVLink / NVSwitch / InfiniBand communication pathsCluster orchestration, RDMA, and bandwidth-optimized compute flowsHigh-performance data pipelines and real-world MLOps infrastructure Book SummaryLarge-Scale AI Engineering is a deep, practical exploration of the systems, hardware, and distributed architectures that make modern AI possible. You’ll learn how GPUs compute, how clusters communicate, why bandwidth dominates training efficiency, and how to design multi-node environments capable of training trillion-parameter models. The book moves from the silicon level, HBM stacks, SM pipelines, FP8 precision, up to orchestration layers, multi-rail networking, and topologies that scale across racks.Across two rich chapters, the book explains how GPUs execute workloads, how the Transformer Engine accelerates large models, and how distributed training frameworks operate across compute, network, and memory domains. By the end, you’ll have a complete understanding of how modern AI infrastructure is built, conceptually, architecturally, and operationally. What’s Inside This Book? (Key Benefits) Understand GPU Architecture & Workloads Design High-Throughput Distributed Training Systems Optimize Communication Across NVLink, NVSwitch & InfiniBand Engineer Multi-Node, Multi-Rack AI Clusters Build Bandwidth-Optimized AI Workflows Gain Practical Insight Into Real-World AI Infrastructure Develop a Complete Systems-Thinking Mindset This Book Is For:Machine Learning engineers who want to understand the systems side of AISoftware developers transitioning into AI infrastructureMLOps engineers building scalable pipelinesData center engineers working with GPU clustersTechnical leaders evaluating or designing AI compute systemsStudents and researchers studying deep learning systems architectureMaster the systems, architecture, and engineering principles behind today’s most powerful AI models. Grab your copy of Large-Scale AI Engineering today and start building the future.
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