AI Fabrics

Overview

AI Fabrics are the specialized network interconnect layers that tie GPU/XPU accelerators together within and across racks, requiring fundamentally different design principles from traditional data center networking. While traditional networks tolerate 50% CPU utilization, AI fabric accelerators must run at 80%+ to be economically justified — networking latency or congestion directly translates to wasted GPU compute time and degraded job completion times.

The AI fabric is structured across three tiers: scale-up (tightly-coupled hundreds of accelerators over copper or short-reach optics, memory-semantic, ultra-low latency), scale-out (longer-distance optical links with oversubscription and packet spraying for multi-rack training), and scale-across (multi-data-center workload partitioning at campus or hyperscale scope). All three tiers must be solved in parallel as cluster sizes grow from today's 100K-XPU deployments toward predicted 1M-XPU superclusters.