How to Transform AI Network Landscape

With the rapid development of artificial intelligence (AI) technology, its requirements for network infrastructure are getting higher and higher. The scale of AI models is constantly increasing, and training and inference tasks pose unprecedented challenges to bandwidth, latency and network efficiency. Against this backdrop, Ethernet network cards, as the core components of network connections, are gradually changing the landscape of artificial intelligence networks through a series of technological innovations.

How can Ethernet network cards completely transform the landscape of artificial intelligence networks

Bandwidth improvement

The training and reasoning of AI models require processing massive amounts of data, which demands that the network have extremely high bandwidth. The latest Ethernet network cards, such as the 400Gbps pcie Gen5.0 Ethernet adapter launched by Broadcom, adopt 5nm process technology, achieving a design with high power and thermal efficiency, and can support data transmission speeds up to 400Gbps. This kind of high-bandwidth network card can effectively reduce data transmission time and improve the training efficiency of AI models.

Low latency and high reliability

AI tasks are highly sensitive to latency, especially in real-time inference scenarios. Ethernet network cards can significantly reduce latency by optimizing the transmission path and adopting advanced congestion control mechanisms. For instance, the X400 Super AI Ethernet solution achieves an end-to-end non-blocking network through packet spraying technology and the order-preserving service of intelligent network cards, significantly reducing network latency. In addition, the AMD Pensandopolla A400 AI network card offers a fast failover function, which can quickly detect and bypass network failures to ensure uninterrupted communication between Gpus.

Multipath transmission and load balancing

The traditional Ethernet transmission scheme has limitations in path selection and is prone to cause network congestion. The new generation of Ethernet network cards, through multipath transmission technologies such as packet spraying and packet spraying, can dynamically distribute data packets to multiple paths, thereby improving network utilization and load balancing capabilities. This technology not only improves the throughput rate of the network, but also reduces the accumulation of data packets, further optimizing the network performance.

Technological innovation of Ethernet network cards

Transport layer protocol innovation

The Super Ethernet Alliance (UEC) is promoting the replacement of the traditional RoCE protocol with the UltraEthernetTransport (UET) protocol. The UET protocol supports multi-path parallel transmission, out-of-order data packet transmission and advanced congestion control mechanisms, which can effectively reduce tail delay and improve network utilization.

Hardware optimization

The new generation of Ethernet network cards has undergone multiple optimizations at the hardware level, including increasing bandwidth, reducing latency and adopting efficient signal transmission technologies. For instance, Broadcom's 400G Ethernet adapter is equipped with the third-generation RoCE pipeline and low-latency congestion control technology, capable of meeting the high-bandwidth and high-pressure demands of AI infrastructure.

Software adaptation

To better support AI and high-performance computing (HPC) workloads, Ethernet network cards have also been deeply adapted at the software level. For instance, UEC has developed apis and data structures that are adapted to AI and HPC workloads, ensuring that Super Ethernet can be seamlessly integrated into existing AI frameworks and HPC libraries.

The influence of Ethernet network cards on AI network architecture

Reshape the network architecture

The technological innovation of Ethernet network cards has made the application of Ethernet in AI networks more extensive. The traditional InfiniBand technology is gradually being replaced by Ethernet in cross-domain expansion scenarios, and Ethernet is also actively exploring adaptation solutions for high-density computing scenarios within the rack. This trend indicates that Ethernet will occupy a more core position in the future AI network architecture.

Enhance network scalability

The scale of AI models is constantly expanding, which puts forward higher requirements for the scalability of the network. Ethernet network cards, by supporting large-scale distributed computing networks, can flexibly expand network resources and meet the requirements of AI model training and inference. For instance, the X400 Super AI Ethernet solution performs exceptionally well in training scenarios with 256-card Gpus, significantly outperforming the traditional RoCE solution.

The future development trend of Ethernet network cards

Higher bandwidth

With the continuous development of AI technology, the demand for network bandwidth will continue to grow. In the future, Ethernet network cards will develop towards higher bandwidths, such as 800G and 1.6Tbps. These high-speed Ethernet technologies will provide more powerful network support for AI and HPC applications.

Intelligence and Automation

The Ethernet network cards of the future will have a higher level of intelligence and be able to automatically perceive the network status and make dynamic adjustments. For instance, the AMD Pensandopolla A400 AI network card can automatically optimize the data transmission path through intelligent multipath technology and path-aware congestion control functions. This intelligent network card will greatly enhance the efficiency and reliability of the network.

The improvement of the ecosystem

The development of Ethernet network cards will promote the improvement of the entire network ecosystem. From hardware manufacturers to software developers, from cloud service providers to AI application developers, all parties will work together to create a more efficient, flexible and open AI network ecosystem.

Summary

Ethernet network cards are completely transforming the landscape of artificial intelligence networks through a series of technological innovations. It not only enhances the bandwidth and performance of the network, but also optimizes the efficiency and reliability of the network through technologies such as multipath transmission, low latency and intelligent management. With the continuous development of technology, Ethernet network cards will play a more important role in future AI networks, providing strong network support for the development of artificial intelligence.