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Artículo: AMZ-B0FGY6FJY6

Optimizing Large Scale AI Workloads with NVIDIA Blackwell:: A Developer’s Guide to the B100 and GB200 Ecosystem

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Optimizing Large Scale AI Workloads with NVIDIA Blackwell: A Developer’s Guide to the B100 and GB200 Ecosystem This developer-focused guide delivers the deep, technical insight required to optimize AI workloads using the B100 GPU and Grace-Blackwell GB200 Superchip, including NVLink Switch Fabric topologies, FP4/FP8 mixed-precision compute, and memory-unified CPU-GPU orchestration. Designed for real-world deployment at scale, this book walks you through the hardware, software, and system-level strategies needed to operate production-grade, future-ready AI infrastructure. Whether you’re deploying LLM inference pipelines, training massive transformer models across NVL72 racks, or building quantum-integrated pipelines, this book offers a clean, practical, and code-aware approach to working at the cutting edge of AI systems. What You’ll Learn Leverage the architecture of the B100 and GB200 for peak performance in AI training and inference Efficiently program and tune kernel execution, memory bandwidth, and mixed-precision compute (FP4, FP8) Architect large-scale distributed training using NVSwitch, NVLink-C2C, FSDP, ZeRO-3, and NCCL Optimize unified memory access across Grace CPUs and B100 GPUs for shared workload efficiency Deploy inference workflows using TensorRT-LLM, Triton Inference Server, and Grace-accelerated pipelines Monitor and debug bottlenecks using Nsight Systems, NVTX, DCGM, and Prometheus Engineer thermally stable, energy-aware infrastructure using NVML, Slurm, Kubernetes, and Grafana Explore hybrid compute with cuQuantum, Omniverse robotics, and digital twin simulation Build production-ready AI environments that scale, recover, and operate intelligently under real-world conditions What Makes This Book Different Strictly Practical: Avoids filler, summaries, and outdated practices—this book is written for engineers who build and optimize, not just read. Technically Deep: Goes beyond documentation, with insights into performance tuning, architecture-aware optimization, and large model orchestration. Code-Informed: Discusses real implementation patterns using up-to-date frameworks like TorchInductor, Megatron-DeepSpeed, Hugging Face Optimum-NVIDIA, and more. Production-Ready: Focuses on real deployment constraints—power, latency, thermal balance, fault tolerance, and integration into existing ML systems. Future-Aligned: Covers digital twins, quantum-class compute, Omniverse simulation, and other extensions of the AI compute landscape. Who This Book Is For AI and ML engineers working on LLMs, vision transformers, or generative workloads Systems and infrastructure developers building GPU clusters, inference platforms, or ML backends MLOps and DevOps professionals responsible for scaling, orchestrating, or monitoring AI services Researchers and technical leaders interested in next-generation compute models and deployment strategies Developers preparing for the shift from Hopper to Blackwell-class hardware and system design This is your definitive guide to building scalable, efficient, and future-proof AI workloads on NVIDIA Blackwell. If you care about performance at the edge of capability, this book belongs on your desk.

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$48,34

49% OFF

$24,79

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