Your Data Center Was Built for Training, Not for What Inference Became

A model can reach a point where the environment that made its development possible no longer offers the right conditions for serving it efficiently, even when the underlying compute hardware remains operational and the original investment still has useful life. The change often begins quietly, as a model acquires longer context requirements, more demanding reasoning paths, additional retrieval operations, or a tighter response-time target that changes how its software uses memory and processors. Training typically concentrates substantial computation into coordinated workloads that move large amounts of data through accelerators, while production inference must respond to requests whose arrival times, input sizes, and computational requirements vary throughout the day. Those different patterns affect processor utilization, memory allocation, network traffic, cooling demand, and the amount of capacity an operator must keep available for unpredictable bursts.