AI Infrastructure Networking Fundamentals

As artificial intelligence models grow in size, the network connecting the underlying hardware becomes the ultimate bottleneck. Understanding how data travels between high-performance computing nodes is essential for building efficient, scalable AI training and inference environments. This text-only course guides you through the foundational networking concepts, protocols, and architectures specifically tailored for modern AI workloads. You will transition from standard enterprise networking principles to high-throughput, low-latency infrastructure design, gaining the knowledge needed to support large-scale machine learning clusters. What you'll learn: 1. Understand core AI networking concepts, including high-bandwidth demands and latency requirements. 2. Compare key transport technologies such as InfiniBand and RDMA over Converged Ethernet (RoCE). 3. Analyze GPU-to-GPU communication patterns and collective communication libraries. 4. Explore modern data center topologies like Leaf-Spine architectures optimized for AI workloads. 5. Configure basic traffic management, congestion control, and network observability strategies. 6. Practice conceptual network design through detailed written scenarios and architectural breakdowns. The course begins with foundational terminology and the unique demands of AI workloads, then progresses to hardware interconnects, advanced transport protocols, and modern cluster design principles. You will learn through clear, written explanations and structured architectural walkthroughs designed for self-paced study. This course is designed for IT professionals, network engineers, and system administrators who are new to AI infrastructure and want to understand how networking supports machine learning. No prior experience with AI hardware is required. Start reading today to master the underlying network architectures that power modern artificial intelligence.