The key role of Ethernet in artificial intelligence networks

Rapid advances in artificial intelligence (AI) technology are revolutionizing the cloud computing and IT industries. Since Chat GPT went live in November 2022, the AI space has experienced an investment boom that has attracted a lot of attention. Major cloud service providers are rolling out new products and services to meet the growing demand for AI, while many large enterprises are actively exploring AI use cases such as generative AI (GenAI) to improve operational efficiency and ROI.

However, the rapid development of AI is placing higher demands on the infrastructure of cloud service providers and enterprise data centers. As the key "fuel" for AI development, data must be effectively collected, protected, and transmitted. Organizations exploring new AI applications must address these challenges. To support the massive amounts of data and computing resources required by AI, we need to build more efficient and reliable network infrastructure.

In this context, Ethernet technology, with its mature and extensive ecosystem, is becoming an important support for AI network infrastructure. Ethernet shows strong potential to meet the high demands of AI and provide a unified platform, which has a significant impact on the economic viability of AI. It enables a consistent operating model across various networks and clouds, avoiding the high costs of maintaining multiple infrastructures.

Key requirements for the development of AI networks

Speed: The rapid growth of AI business is driving the demand for higher speeds in data centers and edge networks, pushing networks toward a new generation of networks such as 400Gbit/s or even 800Gbit/s.
Privacy and security: Networks must process data efficiently while ensuring high-end encryption and security in multi-tenant environments to protect data privacy.
Edge Inference: As enterprises deploy large language models (LLM) or small language models (SLM) and hybrid private AI clouds, front-end deployments of inference capabilities will become a focus.
Short job completion time (JCT) and low latency: Optimizing the network to provide lossless transmission and ensuring efficient bandwidth utilization through congestion management and load balancing is key to achieving fast JCT.
Flexible clustering: In an AI data center, processor clusters can be configured into a variety of topologies. Optimizing performance requires avoiding over-subscription between layers or regions to reduce JCT.
Multi-tenant support: For security reasons, AI networks need to separate data streams.
Standardized architecture: AI networks typically consist of a back-end infrastructure (training) and a front-end (inference). The versatility of Ethernet allows for technology reuse between back-end and front-end clusters.

Continuous innovation in Ethernet technology

Ethernet technology continues to innovate and develop to meet the higher requirements of artificial intelligence for network scale. Some of the key technological advances include:
Packet jetting: This technique allows each network stream to access all paths to the destination simultaneously. Flexible sorting of packets takes full advantage of all Ethernet links for optimal load balancing, forcing sorting only when required for bandwidth-intensive operations in AI workloads.
Congestion management: Ethernet-based congestion control algorithms are critical for AI workloads. They prevent hot spots and distribute the load evenly across multiple paths, ensuring reliable transmission of AI traffic.

Unified and optimized enterprise infrastructure

Enterprises need to deploy a unified AI network infrastructure and operating model to reduce the cost of AI services and applications. Adopting standards-based Ethernet as a supporting technology is a core element. It ensures compatibility between front-end and back-end systems, avoiding the standardization process barriers and economic impact of different architectures. For example, Arista advocates building an "artificial intelligence hub" where Gpus can be efficiently trained through lossless networks. The trained AI models are connected to the AI inference cluster so that end users can easily query these models.

Market advantages of Ethernet

Ethernet, with its openness, flexibility and adaptability, has shown strong competitiveness in AI deployment. Its performance goes beyond InfiniBand, and its benefits will expand further with the Super Ethernet Alliance (UEC) enhancements. In addition, Ethernet is more cost-effective, has a broader and more open ecosystem, and provides commonality, uniform operations and skill sets for both back-end and front-end clusters, as well as platform reuse opportunities between clusters. As AI use cases and services continue to expand, the opportunities for Ethernet infrastructure will increase dramatically, both at the core of hyperscale LLMS and at the edge of the enterprise. Ai-ready Ethernet can meet the demand and provide AI reasoning based on industry-specific private data.

All in all, Ethernet technology plays a crucial role in the AI network infrastructure. It can meet the many needs of AI in terms of speed, security, edge reasoning and so on. Through continuous technological innovation and extensive ecosystem support, Ethernet provides enterprises with more efficient and cost-effective solutions to promote the widespread application and development of artificial intelligence.