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The rise of AI has driven the emergence of multiple new network domains, each with distinct roles, architectures, and performance requirements. This presentation explores these new networks and their roles. Patrick McCabe, representing Nokia, builds on the premise that AI is a permanent fixture in the technological landscape, requiring a non-linear evolution of network architecture. He identifies two primary functions, training and inferencing, as the drivers of this change. Training involves massive GPU clusters that scale geometrically and are highly sensitive to packet loss, while inferencing, often pushed to the network edge, prioritizes low latency to serve end users effectively. Together, these functions demand a move away from traditional statistical averages toward a more deterministic approach to network performance.
Architecturally, the shift from north-south to massive east-west traffic patterns within GPU clusters has rendered traditional leaf-spine designs inadequate for AI data movement. McCabe details the emergence of specialized backend networks categorized as scale-up, scale-out, and scale-across. Scale-up handles communication within a single system or server, while scale-out facilitates high-speed interaction between different systems within a data center, a primary focus for the Ultra Ethernet Consortium (UEC). Scale-across is a particularly challenging new frontier, necessitated by the fragmentation of AI clusters across different physical locations, often due to power constraints, requiring advanced routing and data center interconnects to maintain the illusion of a single compute entity over distances of 10 kilometers or more.
The presentation emphasizes that the center of this new universe is the GPU, supported by essential storage networks that feed vast amounts of data to processing units. While the back end deals with the rigors of scale and reliability, the front end remains more traditional, connecting these specialized environments to the outside world and end users. McCabe concludes with an analogy comparing AI to the printing press, suggesting that while AI lowers the cost and scarcity of production it does not replace the human creator. Instead, it shifts the premium value toward innovation, ideas, and judgment, allowing for a radical expansion of who can create within this high-performance infrastructure.
Personnel: Patrick McCabe
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