Isbn: 9781808080135 - designing nvidia ai infrastructure: gpu compute, networking, orchestration, and security in nvidia's stack, explained (8 resultados)

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Paperback. Condición: new. Paperback. Navigate NVIDIA's enterprise AI infrastructure with confidence, from GPUs and data movement to orchestration, security, monitoring, edge systems, and model serving.Key FeaturesBuild career-relevant knowledge of the NVIDIA AI infrastructure stackMake informed architecture decisions for performance, scalability, security, and costLearn through practical configurations, deployment patterns, and enterprise case studiesBook DescriptionDesigning 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.The 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.Selected 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.What you will learnUnderstand what MIG and vGPU isolate and what they don'tDistinguish RBAC, network policy, and encryption's separate rolesSee how storage, NVLink, and InfiniBand affect GPU utilizationRecognize where Kubernetes tools' responsibilities stopUnderstand how GDPR, HIPAA, and FedRAMP shape AI infrastructure controls and evidenceUse GPU profiling and telemetry data to investigate bottlenecksLearn how NGC, Triton, and ensembles fit a serving pipelineCompare on-prem, cloud, and hybrid AI cluster trade-offsWho this book is forThis book is for infrastructure engineers, ML and MLOps engineers, solutions architects, and technical leads who need a reliable mental model of NVIDIAs 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Paperback. Condición: new. Paperback. Navigate NVIDIA's enterprise AI infrastructure with confidence, from GPUs and data movement to orchestration, security, monitoring, edge systems, and model serving.Key FeaturesBuild career-relevant knowledge of the NVIDIA AI infrastructure stackMake informed architecture decisions for performance, scalability, security, and costLearn through practical configurations, deployment patterns, and enterprise case studiesBook DescriptionDesigning 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.The 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.Selected 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.What you will learnUnderstand what MIG and vGPU isolate and what they don'tDistinguish RBAC, network policy, and encryption's separate rolesSee how storage, NVLink, and InfiniBand affect GPU utilizationRecognize where Kubernetes tools' responsibilities stopUnderstand how GDPR, HIPAA, and FedRAMP shape AI infrastructure controls and evidenceUse GPU profiling and telemetry data to investigate bottlenecksLearn how NGC, Triton, and ensembles fit a serving pipelineCompare on-prem, cloud, and hybrid AI cluster trade-offsWho this book is forThis book is for infrastructure engineers, ML and MLOps engineers, solutions architects, and technical leads who need a reliable mental model of NVIDIAs 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

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Paperback. Condición: new. Paperback. Navigate NVIDIA's enterprise AI infrastructure with confidence, from GPUs and data movement to orchestration, security, monitoring, edge systems, and model serving.Key FeaturesBuild career-relevant knowledge of the NVIDIA AI infrastructure stackMake informed architecture decisions for performance, scalability, security, and costLearn through practical configurations, deployment patterns, and enterprise case studiesBook DescriptionDesigning 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.The 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.Selected 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.What you will learnUnderstand what MIG and vGPU isolate and what they don'tDistinguish RBAC, network policy, and encryption's separate rolesSee how storage, NVLink, and InfiniBand affect GPU utilizationRecognize where Kubernetes tools' responsibilities stopUnderstand how GDPR, HIPAA, and FedRAMP shape AI infrastructure controls and evidenceUse GPU profiling and telemetry data to investigate bottlenecksLearn how NGC, Triton, and ensembles fit a serving pipelineCompare on-prem, cloud, and hybrid AI cluster trade-offsWho this book is forThis book is for infrastructure engineers, ML and MLOps engineers, solutions architects, and technical leads who need a reliable mental model of NVIDIAs 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Taschenbuch. Condición: Neu. Designing NVIDIA AI Infrastructure | GPU compute, networking, orchestration, and security in NVIDIA's stack, explained | Vivian Aranha | Taschenbuch | Englisch | 2026 | Packt Publishing | EAN 9781808080135 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. …