The Shift from Training to Inference

The era of viewing fiber as a passive backbone for uniform compute systems is ending. AI infrastructure demands a new conversation because optical infrastructure now operates as an active architectural layer carrying multiple workload classes with distinct operational characteristics.

For several years, the industry focus remained on training clusters. The priority was building larger models and managing the synchronization demands of thousands of accelerators through dense scale-out fabrics. This required longer optical runs and high-volume east-west traffic. While that model remains important, deployment patterns in 2026 point in a different direction.

Inference has overtaken training as the dominant operational workload. More compute effort is now being spent using models than building them. This shift represents AI maturing from a research-centric discipline into an operational one.

The infrastructure conversation has not yet fully caught up with this reality. Much of the public discussion still revolves around accelerator counts and power consumption, but far less attention focuses on the practical consequences for optical infrastructure, pathway allocation, topology planning, and physical network architecture.

Inference introduces a different set of infrastructure behaviors compared to the training-centric models of the past. As workloads shift, the physical design of the network must adapt to these new operational characteristics.

The industry must move beyond counting GPUs and start addressing how physical network architecture supports the actual use of AI.

How will your network topology change when inference becomes your primary workload?