InfiniBand, RoCE, and All That (fergusfinn.com)

A recent examination of networking technologies details the evolution and significance of InfiniBand and RDMA over Converged Ethernet (RoCE) in AI workloads. InfiniBand, developed in the late 1990s to enable Remote Direct Memory Access (RDMA), optimizes data transfer by bypassing the CPU, thus significantly reducing overhead during tasks like AI training and inference, where GPUs must synchronize massive data transfers efficiently. The standard approach of data handling, which involves multiple kernel buffer copies, falters in these high-stakes environments, leading to the emergence of InfiniBand as the preferred choice for high-performance computing. However, the rise of RoCE, which utilizes Ethernet infrastructure while maintaining lossless characteristics through Data Center Bridging (DCB) and Priority Flow Control (PFC), has shifted the landscape in favor of more economical and widely understood solutions. Companies like Meta have successfully implemented RoCE for large-scale GPU clusters, demonstrating its capability to handle AI workloads competitively with InfiniBand. This transition reflects a broader trend in the AI/ML community toward leveraging existing Ethernet networks, driven by commercial pressures and operational familiarity, while proprietary technologies like InfiniBand remain vital in specialized high-performance applications. The competitive dynamics between these technologies illustrate an ongoing evolution as the AI infrastructure landscape adapts to growing demands.