Energy-Efficient AI for Foundation Models: Algorithms, Hardware, and Data Center Infrastructure

Data centers consumed 415 TWh of electricity in 2024, about 1.5% of global demand, and foundation model training and inference are a growing part of this load. As training and inference continue to grow, energy-efficient foundation models are becoming essential. An efficiency gain may come from the model, the accelerator, or the facility, and existing reviews and primary studies usually address one of these aspects. However, energy is obtained by a different method at each of these stages, so unified and systematic optimization is more difficult than results at any single stage may suggest. Reported metrics range from floating-point operations (FLOPs) and tera-operations per second per watt (TOPS/W) to throughput, power usage effectiveness (PUE), carbon, and water. This review follows energy through each stage and treats each reported value together with its measurement boundary and evidence class. This review covers the chain from how models are designed, compressed, and served through how accelerators execute them at reduced precision to how facilities cool and power them. This review identifies open research problems in wall-plug measurement, cross-layer co-design, lifecycle accounting, and the deployment maturity of emerging accelerators. It aims to serve as a reference for researchers and practitioners seeking a unified view of energy, carbon, and water across foundation model systems.