With the rapid development of Artificial Intelligence (AI) technology, especially the rise of generative AI, the demand for computing and network resources in data centers has reached an unprecedented level. Ethernet (Ethernet) and InfiniBand, as two mainstream network technologies, are providing powerful acceleration for AI applications through continuous innovation and convergence.

Ethernet Evolution, Optical Module Integration and AI Acceleration

Since its inception, Ethernet has become the dominant technology for LANs and WANs with its simple design, low cost and wide range of applications. In recent years, Ethernet has evolved to meet the higher latency and bandwidth requirements of AI and high performance computing (HPC) by introducing several new technologies. Among them, RDMA over Converged Ethernet (RoCE) technology is particularly critical, which allows Remote Direct Memory Access (RDMA) over Ethernet, significantly reducing the latency of network communications, and also dramatically improves data transfer rates and efficiency by integrating high-speed optical modules, such as 400G/800G OSFPs.

In AI applications, RoCE technology enables data to be transferred directly between GPUs without CPU processing, thus greatly improving data transfer efficiency and training speed. In addition, high-end Ethernet switches and cards with powerful congestion control, load balancing and RDMA support can scale to a larger size than traditional networks to meet the needs of large-scale AI model training.

InfiniBand: Designed for High Performance Computing

InfiniBand (Infinite Bandwidth) is a network communication standard designed for high-performance computing, known for its high bandwidth, low latency, and reliable data transmission capabilities. InfiniBand is particularly advantageous in the AI space. It supports RDMA technology, which allows data to be transferred directly between the memories of two computers, thereby reducing the load on the CPU and increasing the efficiency of data transfer.

Another important feature of InfiniBand is its high scalability. It supports a large number of connected nodes and can build complex network topologies such as tree and mesh, providing a flexible network architecture for AI applications. In addition, InfiniBand has an excellent end-to-end congestion control mechanism that automatically adjusts the data flow when the network is congested, ensuring the stability and efficiency of data transmission.

Technology Convergence and Performance Improvement

With the continuous development of AI technology, the boundary between Ethernet and InfiniBand is gradually blurred. Ethernet continues to improve its competitiveness in high-performance computing by introducing new technologies such as RoCE and high-performance optical modules; while InfiniBand is also optimizing its cost-effectiveness and ease of use to attract a wider user base. This trend of technology convergence signals a more diverse and efficient data center network in the future.

In AI applications, the combination of Ethernet and InfiniBand is realizing even more significant performance gains. By deploying a hybrid network architecture, data centers can flexibly choose network technologies according to actual needs and provide an optimal network environment for AI model training. For example, when training large AI models, an InfiniBand network can be used to ensure low-latency and high-bandwidth data transmission, while Ethernet can be used to reduce costs and increase flexibility when handling general data traffic.

AI acceleration in Ethernet and InfiniBand is a key direction in the evolution of data center networking technology. Through continuous innovation and convergence, these two technologies are providing more powerful and efficient network support for AI applications. In the future, with the further development of AI technology and the expansion of application scenarios, we have reason to believe that Ethernet, optical modules and InfiniBand will play an even more important role in the field of AI, driving the data center network forward in the direction of higher speed, lower latency, and more powerful functions.