Building AI Pods with Nexus Hyperfabric from Cisco

This presentation introduces Cisco Nexus Hyperfabric, a cloud-managed platform that simplifies the deployment and ongoing management of AI infrastructure. It addresses the growing need for repeatable, scalable, and operationally efficient networks specifically for enterprise AI clusters. Cisco emphasizes that while hyperscalers build immense AI factories, a significant and growing market exists for smaller, enterprise-level AI deployments, often below 256 nodes, which they term “AI Clusters for the Rest of Us.”

The shift to these smaller, on-premises AI clusters is driven by several factors, including the increasing size and sensitivity of data (e.g., healthcare, intellectual property), making cloud undesirable, a trend of workloads returning from the cloud, and the need for project- or application-specific infrastructure rather than shared general-purpose IT. The rapidly evolving AI technology also means enterprises prefer incremental build-outs rather than massive, infrequent investments, allowing them to leverage newer generations of hardware more frequently. However, designing and deploying these dense, complex, lossless Ethernet networks is challenging and time-consuming for traditional network practitioners, often involving weeks of design, lengthy procurement, and meticulous cabling.

Cisco Nexus Hyperfabric addresses these challenges by delivering a Meraki-like SaaS experience for data center network deployment. It offers pre-designed, NVIDIA ERA-compliant templates for AI clusters that automate the generation of a complete bill of materials, including optics and cables. This drastically reduces design time and eliminates manual errors, accelerating the “time to first token” for AI projects. Hyperfabric also streamlines day-one operations with step-by-step cabling instructions and real-time validation via server-side agents, ensuring correct physical connectivity. Beyond deployment, it provides end-to-end network visibility, proactive monitoring of components such as optics, and integrates advanced Ethernet features, including lossless capabilities (PFC, ECN) and adaptive routing, to optimize performance for demanding AI workloads.