THE DEPLOYMENT ECONOMICS OF PREMIUM INFERENCE

As AI infrastructure moves from headline buildouts to operating discipline, the core question for neoclouds and infrastructure providers is no longer how much compute they can deploy, but how efficiently that capacity can deliver a differentiated service and return capital. This session presents a practical framework for evaluating premium inference through workload fit, service quality, and unit economics: separate prefill from decode, match each phase to the right processor, model utilization and concurrency, account for power, rack, and cooling constraints, and translate latency and throughput into cost to serve, revenue per rack, and time to payback. It will show how heterogeneous systems can be evaluated without reducing the decision to a single benchmark or vendor claim. Attendees will leave with questions and metrics they can use to compare architectures, avoid stranded capacity, and build an inference offering that customers value and operators can sustain.