AI RESEARCH
AI Inference as Relocatable Electricity Demand: A Latency-Constrained Energy-Geography Framework
arXiv CS.AI
•
ArXi:2604.27855v1 Announce Type: cross AI inference is becoming a persistent and geographically distributed source of electricity demand. Unlike many traditional electrical loads, inference workloads can sometimes be executed away from the user-facing service location, provided that latency, state locality, capacity, and regulatory constraints remain acceptable. This paper studies when such digital relocation of computation can be interpreted as latency-constrained relocation of electricity demand. We develop an energy-geography framework for geo-distributed AI inference.