Inference has quietly become the larger AI workload. Here is why that changes what infrastructure buyers should actually be evaluating.
A lot of the public conversation about AI infrastructure still centers on training, the process of building a model. But training isn't where most AI activity actually happens anymore, and that distinction matters more than it sounds. Once a model is built, it moves into production, where it answers real queries, runs enterprise copilots, and powers customer-facing tools around the clock. That ongoing activity is called inference, and it has quietly become the larger workload.
Deloitte estimates inference already accounts for roughly two-thirds of AI compute in 2026, up from a minority share just two years earlier. McKinsey projects inference will represent more than 40 percent of total data center demand by 2030, growing at a 35 percent compound annual rate. AWS and NVIDIA have both stated that inference already accounts for as much as 90 percent of the cost of running large-scale AI workloads at some organizations. This isn't a forecast anymore so much as a description of where the market already stands, and it changes what infrastructure buyers need to evaluate.
What training actually requires
Training is the process of creating and refining a model: feeding it data, adjusting its parameters, and repeating that cycle until performance meets a target. It is computationally intense, GPU-heavy, and centralized by nature, because the hardware needs to exchange massive volumes of data instantaneously to synchronize calculations across thousands of chips. Current-generation systems can draw up to a megawatt per rack during training runs, and that density keeps climbing with each new hardware generation.
Because of these demands, training infrastructure gravitates toward a small number of large, purpose-built facilities, where location matters less for latency and more for available power, land, and cooling capacity. Training clusters end up functioning more like industrial power plants than traditional IT facilities, and they're usually built for burst capacity, intense activity during a training run, followed by periods of lower utilization until the next one begins.
What inference actually requires
Inference is a different problem entirely. It is the ongoing act of running a trained model to answer real requests: a support ticket, a clinical query, a fraud check, a customer conversation. Each individual inference request is computationally lighter than a training cycle, but a production model serves millions of these requests continuously, every hour of every day, with no natural pause.
That changes the infrastructure profile. Inference workloads run at more moderate densities, typically 30 to 150 kilowatts per rack rather than training's megawatt peaks, but they prioritize latency, uptime, and proximity to users over raw centralized power. Inference deployments tend to spread outward toward metro and near-metro locations, optimized for consistent performance rather than periodic bursts. A facility built only to support training bursts is not automatically suited to the different, more constant demands of running that model in production.
Why the distinction matters for compliance-sensitive enterprises
For an enterprise in healthcare, legal, financial services, or government, this distinction carries weight beyond performance. A regulated organization does not train a frontier model from scratch. It fine-tunes or deploys one, then runs it continuously against real patient records, case files, or transaction data. That means the infrastructure question a compliance-sensitive buyer actually faces is an inference question: can this facility support a workload that runs nonstop, in production, with an audit trail that holds up the entire time?
That question has nothing to do with GPU count. A provider can advertise a large training cluster and still fall short on the operational continuity, physical isolation, and access control that a live, regulated inference workload requires day after day. Evaluating an infrastructure provider on training-oriented metrics, when the actual use case is continuous inference, is evaluating the wrong thing.
What this means for infrastructure decisions today
Enterprise buyers evaluating AI infrastructure should ask a different set of questions than the ones the market has trained them to ask. Instead of focusing only on GPU density or raw compute capacity, buyers should confirm that a facility is designed for continuous operation, not just burst performance. They should ask how the provider maintains uptime and audit logging across every hour a model runs in production, not just during a training cycle. And they should recognize that the facility running their AI in production, day in and day out, is doing fundamentally different work than the facility that trained the model in the first place.
Infrastructure built only around the training moment will eventually show its limits once a workload moves into sustained, continuous use, while infrastructure built around inference from the outset never runs into that transition problem.
EG AI Corp is building single-tenant, immersion-cooled AI infrastructure in Dallas-Fort Worth designed for the workload that does not stop: continuous, compliant, and built for production, not just for the training run that came before it.
