The annual Arista Partner Exchange gathering took place in NYC this summer. During the two-day event, the presentation I attended, which was my key takeaway, was Arista's AI Backend Development.
The presentation focused on the complexities and considerations involved in building AI networks, particularly emphasizing the backend over the frontend. There is an ongoing debate among customers about whether to deploy AI models in the cloud or on-premises, noting that while training often occurs in the cloud due to its high GPU requirements and costs, inference can be managed with fewer resources. The backend network, crucial for GPU-to-GPU communication, differs significantly from traditional data center networks due to the intense demands of AI model training. It is important to maintain symmetry and avoid oversubscription in these networks to prevent traffic drops and ensure efficient GPU utilization.
We learned about the architecture of AI networks and the roles of scale-up and scale-out processes. Scale-up involves communication within a server, while scale-out pertains to expanding across multiple systems. The frontend, responsible for data input and model management, requires substantial storage capacity and can handle mixed traffic types. In contrast, the backend demands a highly specialized and isolated network to support synchronous GPU operations, with a focus on minimizing latency and ensuring lossless data transmission. The presentation also touched on the challenges of managing power consumption and the necessity of advanced cooling solutions to accommodate the high energy demands of AI systems.
Throughout the presentation, the emphasis was on the critical role of visibility and telemetry in managing AI networks. Tools like Arista's Cloud Vision provide dashboards to monitor network performance, congestion, and job status, helping to preemptively address potential issues. The presentation concluded with a discussion on the advantages of using Ethernet over Infiniband for AI networks, citing Ethernet's broader support, vendor diversity, and ease of integration with existing infrastructure.
The presentation highlighted the intricate demands of building AI networks, emphasizing the critical need for specialized backend infrastructure to support efficient GPU communication and training processes. It underscored the advantages of Ethernet for AI networks, advocating for tailored solutions to meet diverse customer needs while ensuring optimal performance and reliability.
I look forward to what Arista brings in the AI space. Arista Networks has been recognized in the Visionaries Quadrant of the 2025 Gartner® Magic Quadrant™ for Enterprise Wired and Wireless LAN Infrastructure published on 26 June 2025. Gartner positioned Arista Networks as the vendor with the highest Ability to Execute in the Visionaries Quadrant in the report. We shall see what the future holds.
