Scale-up, scale-out, and scale-across connectivity models are redefining how GPUs, racks, and data centers are connected, creating opportunities across the full AI networking infrastructure stack. With advancements in Ethernet, high-speed silicon interconnects, coherent optics, network automation, and more, there seems to be no limit in networking innovations being built to satiate the demands of AI.
Building on these established models, the session will also explore emerging “scale-beyond” architectures, where intelligent inference placement extends compute to the edge and across distributed environments.This introduces a new networking dimension that dynamically aligns this new paradigm of distributed compute driving both performance and scale but also optimizing business and economic outcomes.
The webinar and report will examine how operators, hyperscalers, and technology vendors are evolving architectures to support both traditional AI scaling models and next-generation inference-driven network intelligence.
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