Exploring the Ideal Switches for Artificial Intelligence

With the rapid development and widespread application of artificial intelligence (AI), the high demands AI places on network performance have become a crucial challenge in today's technological advancement. Choosing switches that are suitable for AI applications is essential. This article will discuss the challenges AI poses to network performance and introduce switch solutions suitable for artificial intelligence.

AI applications require exceptional network performance. Here are the challenges AI poses to network performance.

Firstly, high throughput and low latency are fundamental requirements for AI tasks. Fast data transmission and low latency are crucial due to the significant amount of data involved in AI tasks. Secondly, AI applications demand reliability and stability in data accuracy, making these qualities crucial considerations in network design.

Traditional TCP/IP protocols have certain limitations when faced with the demands of AI applications. Firstly, TCP/IP protocols introduce significant delays in data transmission due to multiple context switches and CPU involvement in packet encapsulation. Secondly, TCP/IP networks place a heavy load on host CPUs, largely due to the high coefficient of correlation between network bandwidth and CPU utilization. Additionally, the traditional three-layer network architecture suffers from bandwidth wastage and limitations in large-scale data transmission and processing, necessitating alternative solutions better suited for AI applications.

The traditional three-layer network architecture (access layer, aggregation layer and core layer) has certain drawbacks and limitations when it comes to AI applications. And with the development of cloud computing, these shortcomings have become more prominent, including waste of bandwidth, large fault domain and long latency.

To optimize network performance, the leaf-spine architecture has emerged as a superior choice. The leaf-spine architecture directs network traffic directly to the target device, reducing bandwidth wastage and providing lower latency and better scalability. Optimizing network architecture can meet the high demands of AI applications on network performance and improve the efficiency and performance of AI applications.

Remote Direct Memory Access (RDMA) technology has emerged to meet the network performance demands of AI applications. RDMA enables direct data transfer between host memory and network devices, bypassing the CPU and thereby reducing latency and alleviating CPU loads. In Ethernet-based RDMA solutions, technologies such as Infiniband, RoCE, and iWARP have become prominent choices. Among them, Infiniband is specially designed for RDMA, ensuring reliable transmission from the hardware level. It has advanced technology, but the cost is high. Both RoCE and iWARP are based on Ethernet RDMA technology. These technologies support high throughput, low latency, and reliable transmission, providing more efficient network performance for AI applications. For more information about RDMA, please refer to A Quick Look at the Differences: RDMA vs TCP/IP.

Selecting switches suitable for AI requires considering multiple factors. Firstly, the switches should support RDMA technology to meet the high throughput and low latency requirements. Secondly, switches should possess scalability and flexibility to accommodate the growing workload of AI. There are various options available in the market, including custom AI switch solutions provided by manufacturers like NVIDIA.

NVIDIA Spectrum and Quantum platforms are deployed with both Ethernet and InfiniBand switches. The Spectrum and Quantum platforms target different application scenarios. Spectrum-X is designed for generative AI, optimizing the limitations of traditional Ethernet switches. In NVIDIA's vision, AI application scenarios can be roughly divided into AI cloud and AI factory. In the AI cloud, traditional Ethernet switches and Spectrum-X Ethernet can be used, while in the AI factory, the NVLink+InfiniBand solution needs to be used. For more information about NVLink, please refer to An Overview of NVIDIA NVLink.

The following table shows the original NVIDIA switches provided by FS.

AI applications pose high demands on network performance, and switches, as core components of the network, are crucial for meeting these demands. This article has discussed the challenges AI presents to network performance and introduced switch solutions suitable for artificial intelligence. By adopting RDMA technology and optimizing network architecture, high throughput, low latency, and reliable transmission can be achieved, meeting the requirements of AI applications. Choosing switches that are suitable for artificial intelligence is a critical step in enhancing AI network performance and efficiency. In the future, as AI technology continues to evolve, innovative network devices and architectures will drive further advancements in AI applications.