AI workloads demand unprecedented levels of bandwidth, low latency, and deterministic communication across increasingly dense compute infrastructures. This work focuses on emerging network architectures tailored for AI servers, racks, and clusters—highlighting trends such as high-radix topologies, RDMA over converged Ethernet (RoCE), optical interconnects, and in-network compute. It examines how networking shapes system performance, scalability, and efficiency, and outlines architectural strategies to address bottlenecks in collective communication, model parallelism, and distributed training at hyperscale.
