AI Inference as Relocatable Electricity Demand: A Latency-Constrained Energy-Geography Framework
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. The framework models a three-layer architecture of clients, service nodes, and compute nodes, and formulates inference placement as a constrained optimizatio...
| Authors | Xubin Luo, Cheng Yang |
|---|---|
| Primary category | cs.DC |
| Published | 30 April 2026 |
| Last updated at source | 1 May 2026 |
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