AI Training vs Inference: The Workload Split
Training and inference are two sides of the AI compute coin with very different energy profiles. Training is bursty and concentrated; inference is steady, distributed, and — once a model is deployed at scale — usually the bigger cumulative energy line.
AI training is the process of fitting model parameters to data: a one-time (per model generation) campaign that runs for days to months on a tightly coupled cluster of GPUs or specialised accelerators. Power draw is bursty — GPUs swing 30 to 60 percent between matrix-multiplication, all-reduce communication, and checkpoint phases on millisecond timescales — which stresses on-site UPS and harmonic filtering more than average wattage figures suggest. Frontier training runs in the mid-2020s draw tens to hundreds of megawatts continuously over weeks. AI inference is the act of running an already-trained model on a user request. Each call is comparatively cheap — milliseconds to seconds of GPU time — but inference runs forever and scales with users, not with model count. Industry analyses by 2025 estimated that inference already consumes the majority of large-language-model lifecycle energy at major providers, and forecasters expected the share of accelerator capacity dedicated to inference to exceed 80% by 2028. The practical consequence: training drives where new hyperscale campuses are sited (near cheap, reliable power) while inference drives how dense and how distributed the edge of the network must be (near users, with low latency). Both load types coexist in the same facilities but they have different grid-planning and power usage effectiveness implications.