Modern AI clusters require far more than high-speed switching. Large GPU deployments combine edge networking, Ethernet fabrics, InfiniBand, NVLink, and host/DPU networking into a tightly coupled system that must operate as a single platform. As clusters scale, traditional network operations and homegrown automation become increasingly difficult to sustain.

 

Drawing on lessons learned from deploying more than 35 production multi-tenant GPU clouds and AI factories, this session examines the networking challenges unique to AI infrastructure. Topics include managing the five layers of AI networking, the operational importance of hardware-enforced multi-tenancy, why many organizations outgrow DIY automation, and how accumulated deployment experience helps reduce operational risk across evolving GPU architectures. Attendees will gain practical insights into designing networking operations that support scalable, secure, and repeatable AI infrastructure for cloud providers, enterprise AI factories, and HPC environments.