AI infrastructure shifts to multi-architecture fabric; optical-copper interconnects define system performance

AI infrastructure has historically been dominated by the compute chip itself—the accelerator that runs the model. As deployments have grown from individual servers to racks, multi-rack clusters, and pod-scale systems, the bottleneck has shifted upstream to the interconnect fabric that binds these resources together. Recent product launches by Nvidia, AMD, and Google confirm this transition: each new system integrates compute, memory, storage, and interconnect as a complete unit, with the rack as the new minimum deployment level.

The AI fabric itself is a complex supply chain spanning multiple technologies and layers. Scale-up interconnects (within a node or rack) use high-speed connections, while scale-out links between clusters rely on network interface cards (NICs), digital packet units (DPUs), and leaf-spine switching architectures. The physical path includes both electrical components (PCIe, copper interconnects) and optical components (modules, cables, connectors), each with its own specialized suppliers and performance constraints.

A critical shift is underway: as cloud service providers (CSPs) begin designing their own custom ASICs for AI workloads, the fabric landscape is becoming more heterogeneous. This customization expands the total addressable market for interconnect vendors—no longer can a single standard (like Nvidia's NVLink) dominate the entire ecosystem. Instead, optical-copper interconnect specialists, high-speed cable makers, and laser module suppliers are positioned to gain share across multiple competing architectures. For Taiwanese suppliers in particular, this fragmentation creates new opportunities in the physical interconnect supply chain.