{"product_id":"designing-nvidia-ai-infrastructure-gpu-compute-networking-orchestration-and-security-in-nvidias-stack-explained-paperback","title":"Designing NVIDIA AI Infrastructure: GPU compute, networking, orchestration, and security in NVIDIA's stack, explained - Paperback","description":"\u003cp\u003eby \u003cb\u003eVivian Aranha\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eNavigate NVIDIA's enterprise AI infrastructure with confidence, from GPUs and data movement to orchestration, security, monitoring, edge systems, and model serving.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- Build career-relevant knowledge of the NVIDIA AI infrastructure stack\u003c\/p\u003e\u003cp\u003e- Make informed architecture decisions for performance, scalability, security, and cost\u003c\/p\u003e\u003cp\u003e- Learn through practical configurations, deployment patterns, and enterprise case studies\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBook Description: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eDesigning NVIDIA AI Infrastructure is a concise reference guide for professionals who want to develop career-relevant knowledge of GPU-powered platforms without working through a lengthy manual.\u003c\/p\u003e\u003cp\u003eThe book explains how CPUs, GPUs, DPUs, storage, networking, software, and orchestration combine to support AI workloads. You will explore MIG and vGPU resource models, Kubernetes and Slurm scheduling, data pipelines, performance profiling, monitoring, TensorRT optimization, multi-tenant security, and governance. You will also learn how NVIDIA Jetson and Orin support edge AI and how NGC and Triton Inference Server contribute to model deployment and scalable serving.\u003c\/p\u003e\u003cp\u003eSelected commands, configuration examples, architecture diagrams, and enterprise scenarios connect these technologies to operational contexts. By the end, you will be able to discuss the NVIDIA AI infrastructure stack with greater confidence, evaluate common design choices and bottlenecks, and use the book as a quick reference when planning cloud, on-premises, hybrid, and edge AI environments.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat You Will Learn: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- Understand what MIG and vGPU isolate and what they don't\u003c\/p\u003e\u003cp\u003e- Distinguish RBAC, network policy, and encryption's separate roles\u003c\/p\u003e\u003cp\u003e- See how storage, NVLink, and InfiniBand affect GPU utilization\u003c\/p\u003e\u003cp\u003e- Recognize where Kubernetes tools' responsibilities stop\u003c\/p\u003e\u003cp\u003e- Understand how GDPR, HIPAA, and FedRAMP shape AI infrastructure controls and evidence\u003c\/p\u003e\u003cp\u003e- Use GPU profiling and telemetry data to investigate bottlenecks\u003c\/p\u003e\u003cp\u003e- Learn how NGC, Triton, and ensembles fit a serving pipeline\u003c\/p\u003e\u003cp\u003e- Compare on-prem, cloud, and hybrid AI cluster trade-offs \u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWho this book is for: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eThis book is for infrastructure engineers, ML and MLOps engineers, solutions architects, and technical leads who need a reliable mental model of NVIDIA's AI infrastructure stack before designing, evaluating, or securing a GPU platform. It also suits professionals moving into AI infrastructure roles. Familiarity with Linux, containers, networking, cloud computing, or Kubernetes is helpful; advanced model-development knowledge and access to enterprise GPU hardware are not required.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eTable of Contents\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- Foundations of AI Infrastructure\u003c\/p\u003e\u003cp\u003e- GPU Resource Management and Virtualization\u003c\/p\u003e\u003cp\u003e- Storage, Networking, and Data Pipelines for AI\u003c\/p\u003e\u003cp\u003e- AI Cluster Orchestration and Scalability\u003c\/p\u003e\u003cp\u003e- Performance Optimization and Monitoring\u003c\/p\u003e\u003cp\u003e- Security, Compliance, and Data Governance\u003c\/p\u003e\u003cp\u003e- Edge AI Infrastructure and Integration\u003c\/p\u003e\u003cp\u003e- NGC, Triton Inference Server, and Deployment\u003c\/p\u003e\u003cp\u003e- Real-World AI Infrastructure and Enterprise Workflows\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 134\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.29 x 9.25 x 7.5 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e August 28, 2026\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":71808532513021,"sku":"9781808080135","price":64.58,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/3414\/0157\/files\/zxGOFB7vxC9781808080135.webp?v=1793814644","url":"https:\/\/booktolia.com\/products\/designing-nvidia-ai-infrastructure-gpu-compute-networking-orchestration-and-security-in-nvidias-stack-explained-paperback","provider":"booktolia","version":"1.0","type":"link"}