Networking for AI Infrastructure: Low-Latency Cluster Design

As AI models grow, the network becomes the ultimate bottleneck. Standard networking setups cannot handle the massive, continuous data transfers required for distributed GPU training and high-throughput inference. This text-based course guides you through the fundamental networking concepts, architectures, and protocols required to build and maintain high-performance AI infrastructure. By completing this course, you will understand how to eliminate latency and maximize throughput for deep learning workloads. What you'll learn: - Understand the foundational principles of high-bandwidth, low-latency networking designed specifically for AI workloads - Explore key technologies like RDMA over Converged Ethernet (RoCEv2) and InfiniBand for efficient GPU-to-GPU communication - Configure cloud networking parameters to minimize packet loss and optimize throughput during distributed training - Analyze modern cluster topologies, including non-blocking Clos networks, to ensure seamless scalability - Implement basic network observability and performance monitoring tools to troubleshoot bottlenecks You will start with core networking definitions and terminology before progressing to practical deployment strategies and modern optimization techniques for multi-node clusters. This course is designed for aspiring infrastructure engineers, network administrators, and system architects new to AI networking, with no advanced prerequisites required. Read on to build the networking foundation needed to power modern AI systems.