AI Data Center Network Design and Technologies, 1st edition

Published by Addison-Wesley Professional (January 25, 2026) © 2026

  • Mahesh Subramaniam
  • Michal Styszynski
  • Himanshu Tambakuwala
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Title overview

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.

Table of contents

    Foreword.. . . . . . . . . . . . . . . . xv

    Preface.. . . . . . . . . . . . . . . . . xvii

    Acknowledgments.. . . . . . . . . . . . . . xix

    About the Authors.. . . . . . . . . . . . . . xxi

1 Wonders in the Workload. . . . . . . . . . . . 1

    What’s New in AI Data Center Workloads.. . . . . . . . 1

    The Life Cycle of an AI Model.. . . . . . . . . . . 2

        Training an AI Model. . . . . . . . . . . . 3

    Parallelism. . . . . . . . . . . . . . 4

    Job Completion Time (JCT). . . . . . . . . . . 6

    Tail Latency.. . . . . . . . . . . . . . 7

    Summary. . . . . . . . . . . . . . 16

    Test Your Knowledge. . . . . . . . . . . . 17

2 “The Common-Man View” of AI Data Center Fabrics.. . . . . 19

    Training vs. Inference AI Data Centers. . . . . . . . . 19

    InfiniBand vs. Ethernet for AI Training Data Centers.. . . . . . 21

    Ethernet Hardware Switches and Advanced Software Features.. . . . 22

    Handling Elephant Flows.. . . . . . . . . . . 24

    Load-Balancing Techniques. . . . . . . . . . . 25

    Congestion Management and Mitigation Techniques.. . . . . . 26

    Summary. . . . . . . . . . . . . . 28

    Test Your Knowledge. . . . . . . . . . . . 29

3 Network Design Considerations. . . . . . . . . . 31

    Background Introduction.. . . . . . . . . . . 31

    Training Data Center Architecture. . . . . . . . . . 33

    Rail-Optimized Design (ROD).. . . . . . . . . . 34

    Rail-Unified Design (RUD).. . . . . . . . . . . 42

    Rack Design. . . . . . . . . . . . . . 45

    Scheduled Fabric. . . . . . . . . . . . . 49

    Topologies. . . . . . . . . . . . . . 50

    Inference Data Center Architecture. . . . . . . . . 56

    Multi-Planar Scale-Out Architectures.. . . . . . . . . 56

    Summary. . . . . . . . . . . . . . 63

    Test Your Knowledge. . . . . . . . . . . . 64

    References. . . . . . . . . . . . . . 66

4 Optics and Cable Management.. . . . . . . . . . 67

    Scaling Optics for AI Clusters.. . . . . . . . . . 67

    Challenges in Optical Innovation.. . . . . . . . . . 70

    Packet Flow. . . . . . . . . . . . . . 70

    Transmission Modes.. . . . . . . . . . . . 73

    Transceiver Types.. . . . . . . . . . . . . 76

    Cable and Connector Types. . . . . . . . . . . 78

    Standards.. . . . . . . . . . . . . . 79

    Further Innovations in Optics.. . . . . . . . . . 82

    Summary. . . . . . . . . . . . . . 83

    Test Your Knowledge. . . . . . . . . . . . 85

    References. . . . . . . . . . . . . . 86

5 Thermal and Power Efficiency Considerations. . . . . . . 87

    Thermal Footprints in AI Data Centers.. . . . . . . . . 87

    Airflow Options. . . . . . . . . . . . . 88

    Liquid Cooling. . . . . . . . . . . . . 89

    Summary. . . . . . . . . . . . . . 93

    Test Your Knowledge. . . . . . . . . . . . 94

    References. . . . . . . . . . . . . . 95

6 Efficient Load Balancing. . . . . . . . . . . . 97

    Per-Flow Load Balancing. . . . . . . . . . . 99

    Per-Packet Load Balancing.. . . . . . . . . . . 115

    Load-Balancing Mechanism Comparison.. . . . . . . . 117

    Summary. . . . . . . . . . . . . . 118

    Test Your Knowledge. . . . . . . . . . . . 119

7 RoCEv2 Transport and Congestion Management.. . . . . . 123

    Congestion Points. . . . . . . . . . . . 123

    Explicit Congestion Notification (ECN).. . . . . . . . 127

    Data Center Quantized Congestion Notification (DCQCN).. . . . . 134

    Source Flow Control (SFC). . . . . . . . . . . 136

    Congestion Signaling.. . . . . . . . . . . . 137

    Summary. . . . . . . . . . . . . . 139

    Test Your Knowledge. . . . . . . . . . . . 140

8 IP Routing for AI/ML Fabrics.. . . . . . . . . . 143

    Dynamic IP Routing Options. . . . . . . . . . 144

    eBGP Underlay for Three-Stage/Five-Stage Fabric for an AI Data Center.. . 145

    Multi-tenancy for an AI/ML Cluster Data Center Network. . . . . 171

    Microsegmentation and Multi-tenancy for an AI/ML Data Center.. . . 177

    Extending IP Routing to the Server. . . . . . . . . 177

    Traffic Engineering in the AI Data Center Fabric.. . . . . . . 178

    Segment Routing and SRv6 for AI/ML Fabrics. . . . . . . 179

    Summary. . . . . . . . . . . . . . 184

    Test Your Knowledge. . . . . . . . . . . . 185

    References. . . . . . . . . . . . . . 187

9 Storage Network Design and Technologies.. . . . . . . 189

    The AI Data Center Life Cycle and Storage Networks.. . . . . . 191

    Storage Network Design Types. . . . . . . . . . 193

    Block, Object, and File Storage Systems.. . . . . . . . 198

    NVMe-oF for Block-Level Access.. . . . . . . . . . 199

    NVMe-o-RDMA/RoCEv2 State Machine. . . . . . . . 206

    High-Performance File Systems. . . . . . . . . . 208

    GPUDirect Storage.. . . . . . . . . . . . 211

    Summary. . . . . . . . . . . . . . 217

    Test Your Knowledge. . . . . . . . . . . . 218

    References. . . . . . . . . . . . . . 219

10 AI Network Performance KPIs. . . . . . . . . . 221

    Significance of Performance Benchmarking. . . . . . . 221

    MLCommons for AI Data Centers.. . . . . . . . . 223

    MLCommons Initiatives. . . . . . . . . . . 224

    MLCommons Benchmarking Suites.. . . . . . . . . 224

    Benchmarking a Data Center for Machine Learning. . . . . . 225

    Summary. . . . . . . . . . . . . . 226

    Test Your Knowledge. . . . . . . . . . . . 227

    References. . . . . . . . . . . . . . 228

11 Monitoring and Telemetry.. . . . . . . . . . . 229

    Exploring Monitoring Options.. . . . . . . . . . 229

    Network Monitoring in an AI/ML Data Center Network.. . . . . 231

    In-Band Flow Analyzer (IFA). . . . . . . . . . . 234

    Corrective Actions. . . . . . . . . . . . 237

    Summary. . . . . . . . . . . . . . 238

    Reference.. . . . . . . . . . . . . . 238

12 Ultra Ethernet Consortium (UEC). . . . . . . . . 239

    UEC Developments and Working Groups.. . . . . . . . 241

    UEC Key Terminology.. . . . . . . . . . . . 244

    The UEC and Network Architectures. . . . . . . . . 246

    A New Protocol Stack.. . . . . . . . . . . . 247

    Data Plan: Packet Forwarding Options.. . . . . . . . 252

    Packet Delivery Modes.. . . . . . . . . . . 257

    Congestion Management (CM) in the UEC Specification.. . . . . 261

    Packet Trimming and Fast Retransmissions. . . . . . . . 264

    Link Layer Reliability (LLR) Mechanism.. . . . . . . . 265

    In-Network Collectives (INC) and xCCL.. . . . . . . . 266

    Management and Orchestration. . . . . . . . . . 268

    Interoperability and Backward Compatibility.. . . . . . . 269

    Compliance and Certification.. . . . . . . . . . 269

    UEC Challenges and Future Directions.. . . . . . . . 269

    Comparing UEC to InfiniBand and RoCEv2. . . . . . . . 270

    Summary. . . . . . . . . . . . . . 271

    Test Your Knowledge. . . . . . . . . . . . 272

    References. . . . . . . . . . . . . . 273

13 Scale-Up Systems.. . . . . . . . . . . . . 275

    Key Building Blocks of Scale-Up Systems.. . . . . . . . 278

    Scale-Up Ethernet Transport (SUE-T). . . . . . . . . 281

    Ultra Accelerator Link (UALink).. . . . . . . . . . 286

    Memory Coherence in Scale-Up Systems.. . . . . . . . 291

    Scale-Up Systems: Key Differences and Similarities.. . . . . . 292

    Summary. . . . . . . . . . . . . . 294

    Test Your Knowledge. . . . . . . . . . . . 295

    References. . . . . . . . . . . . . . 297

14 Conclusion.. . . . . . . . . . . . . . 299

    DC Network Role for AI.. . . . . . . . . . . 299

    Caveats and Challenges.. . . . . . . . . . . 300

    Future Developments.. . . . . . . . . . . . 302

    Final Remarks.. . . . . . . . . . . . . 304

    References. . . . . . . . . . . . . . 305

Appendix A Questions and Answers.. . . . . . . . . . 307

Appendix B Acronyms.. . . . . . . . . . . . . 329

 

9780135436288, TOC, 1/8/2026

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