AI Data Center Network Design and Technologies

Description

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 environmentswhere traditional data center designs simply cant 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, YOULL 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 workbut why they must evolve.

Sample Content

Table of Contents

Part 1: AI/ML Data Center Design Workloads and Requirements

Chapter 1 Wonders in the Workload

Chapter 2 The Common-Man View of AI Data Center Fabrics

Part 2: AI/ML Data Center Design Concepts

Chapter 3 Network Design Considerations

Chapter 4 Optics and Cables Management

Chapter 5 Thermal and Power Efficiency Considerations

Part 3: AI/ML Data Center Technology Requirements

Chapter 6 Efficient Load Balancing

Chapter 7 RoCEv2 Transport and Congestion Management

Chapter 8 IP Routing for AI/ML Fabrics

Chapter 9 Storage Network Design and Technologies

Part 4: KPIs and Performance Monitoring

Chapter 10 AI Network Performance KPIs

Chapter 11 Monitoring and Telemetry

Part 5: UEC Ultra Ethernet Consortium 

Chapter 12 Ultra Ethernet Consortium (UEC)

CONCLUSION

Chapter 13 Scale-Up Systems

Chapter 14 Conclusion

Appendix A: Questions and Answers

Appendix B: Acronyms

Updates

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