NVIDIA GPU Infrastructure Fundamentals

NVIDIA GPU Infrastructure Fundamentals is a structured book on NVIDIA GPU systems, spanning CUDA, hardware, networking, storage, orchestration, MLOps, inference, and production operations. It is aimed at readers who need to compare infrastructure components and understand their roles across AI workflows, rather than learn only one tool.

At a glance

Covers CUDA and the NVIDIA software ecosystem alongside GPU architecture and platform selection.
Explains resource sharing and monitoring with MIG, vGPU, and DCGM, plus scheduling with Kubernetes and Slurm.
Connects networking and storage topics such as Ethernet, InfiniBand, RDMA, GPUDirect Storage, BlueField DPUs, and DOCA.
Surveys MLOps, software delivery, and inference tools including Airflow, MLflow, Kubeflow, NGC, ONNX, TensorRT, and Triton.
Includes production-cluster operations, scaling, monitoring, diagnostics, and a chapter preparing readers for the NCA-AIIO exam.

Price

$28.00Save 38%

Reference price: ~~$44.99~~

Price checked: Nov 14, 2026

View offer Ahead of Black FridayExplore the selection

Often bought together

Designing NVIDIA AI Infrastructure: GPU compute, networking, orchestration, and security in NVIDIA's stack, explained

$34.00

Pros and cons

Pros

Broad, connected coverage helps readers compare technologies across compute, networking, operations, and the AI lifecycle.
The listing says programming, data science, and prior GPU experience are not required, while basic IT, cloud, or data-center familiarity is helpful.
At 236 pages, the listed scope is a compact introduction to a wide infrastructure stack.

Technical specifications

8 specifications

Book details
Publisher	Packt Publishing
Publication date	August 31, 2026
Edition	1st
Language	English
Print length	236 pages
ISBN-13	978-1808087493

Dimensions and installation
Weight listed	14.6 ounces
Dimensions	7.5 x 0.54 x 9.25 inches

Who is it for?

A good fit for

System administrators and cloud or DevOps professionals moving into AI infrastructure.
Data-center and networking teams, solution architects, technical managers, and presales professionals evaluating NVIDIA GPU platforms.
Beginners with basic IT, cloud, or data-center familiarity who want an overview without requiring programming or prior GPU experience.

Less suited to

Readers seeking hands-on coding exercises, a deep specialist treatment of one tool, or a practical deployment manual. The listing emphasizes a broad conceptual guide and does not establish that these are included.

NVIDIA GPU Infrastructure Fundamentals · photo 2 — Additional product photo 2

Customer reviews on Amazon

4.8 / 5 · 7 ratings

5 stars82%82%
4 stars18%18%
3 stars0%0%
2 stars0%0%
1 star0%0%

Aggregate customer ratings are positive, with ratings shown in the four- and five-star categories. The sample is small and no written feedback is supplied, so it cannot show which topics readers liked or how well the book serves different experience levels. The listed breadth may suit readers seeking an overview, but ratings alone do not establish its technical depth or practical usefulness.

Frequently asked questions

What topics does the book cover?

The listing describes coverage from accelerated computing, CUDA, and GPU selection through resource sharing, scheduling, networking, storage, MLOps, inference, and production operations.

Is prior programming or GPU experience required?

The listing says programming, data science, and previous GPU experience are not required. It says basic familiarity with IT, cloud, or data-center concepts is helpful.

Does this book prepare readers for the NCA-AIIO exam?

The contents include a chapter titled around preparing for the NCA-AIIO exam, but the listing does not establish that the book alone guarantees exam readiness.

How long is the book?

The listing gives a print length of 236 pages.