High-Performance Networking with InfiniBand for AI and HPC

Modern scientific research and AI models require massive computational power, but traditional networks often become the bottleneck. Understanding how high-performance networking technologies like InfiniBand solve these throughput and latency challenges is essential for building modern data centers. This written course guides you through the core concepts of InfiniBand architecture, from basic physical layer standards to advanced clustering for AI workloads. You will gain the foundational knowledge needed to plan, deploy, and maintain high-throughput networks that power scientific innovations and large-scale machine learning. What you'll learn: Understand the fundamental architecture of InfiniBand and how it differs from traditional Ethernet networks; Explore Remote Direct Memory Access (RDMA) and its role in reducing latency and CPU overhead; Configure partitioning, subnets, and routing mechanisms to optimize cluster performance; Analyze how modern AI workloads and GPU clustering utilize InfiniBand for distributed training; Compare InfiniBand with RDMA over Converged Ethernet (RoCE) to make informed infrastructure decisions; Practice troubleshooting common network performance bottlenecks through written scenarios and diagnostic walkthroughs. You will start with key terminology, network topologies, and foundational hardware components before diving into subnet management, quality of service, and modern observability practices. The material concludes with practical architectural patterns for scaling AI and scientific computing clusters. This course is designed for system administrators, network engineers, and IT professionals new to high-performance computing, requiring no prior experience with InfiniBand hardware. Start reading today to master the networking technology behind the world's fastest supercomputers.