Artificial intelligence is redefining the scale, architecture, and performance expectations of modern data centers. Training large ML models demand infrastructure capable of moving massive data sets through highly parallel, compute-intensive environments—where traditional data center designs simply can’t keep up.
AI Data Center Network Design and Technologies is the first comprehensive, vendor-agnostic guide to the design principles, architectures, and technologies that power AI training and inference clusters. Written by leading experts in AI Data center design, this book helps engineers, architects, and technology leaders understand how to design and scale networks purpose-built for the AI era.
INSIDE, YOU’LL LEARN HOW TO
- Architect scalable, high-radix network fabrics to support xPU (GPE, TPU)-based AI clusters
- Integrate lossless Ethernet/IP fabrics for high-throughput, low-latency data movement
- Align network design with AI/ML workload characteristics and server architectures
- Address challenges in cooling, power, and interconnect design for AI-scale computing
- Evaluate emerging technologies from the Ultra Ethernet Consortium (UEC) and their affect on future AI data centers
- Apply best practices for deployment, validation, and performance measurement in AI/ML environments
With broad coverage of both foundational concepts and emerging innovations, this book bridges the gap between network engineering and AI infrastructure design. It empowers readers to understand not only how AI data centers work—but why they must evolve.
