The selection of an appropriate networking technology is crucial for efficient and high-performance artificial intelligence (AI) workloads. Two primary contenders in this space are InfiniBand and Ethernet, each offering distinct characteristics that impact data transfer rates, latency, and overall system performance. Choosing between these options necessitates a thorough understanding of their underlying architectures and suitability for specific AI applications.
High-performance computing, including AI, benefits significantly from low-latency and high-bandwidth interconnects. Historically, InfiniBand has been favored for its RDMA (Remote Direct Memory Access) capabilities, enabling direct memory access between nodes, minimizing CPU overhead and maximizing data throughput. Ethernet, on the other hand, has the advantage of ubiquity, cost-effectiveness, and ongoing advancements such as RoCE (RDMA over Converged Ethernet), which attempts to bridge the performance gap. The availability of mature infrastructure and widespread support often makes Ethernet an attractive option, especially in environments where existing network infrastructure can be leveraged.
