The rapid growth of foundation models, most prominently large language models (LLMs), is turning the electricity consumption of computing infrastructure into a first-order constraint on the artificial intelligence (AI) industry. This review synthesizes recent literature on three levers for reconciling large-model computing with power-system limits: green energy supply, green computing, and computing-energy synergy (CES), i.e., the coordinated operation of computing infrastructure and power systems. Beyond earlier descriptive accounts, the review makes four contributions: (1) comparative assessments that condense the energy-supply options for AI data centers and the principal energy-efficiency techniques, including quantization, pruning, knowledge distillation, batching, dynamic voltage and frequency scaling (DVFS), and scheduling, into single-view tables with their performance, latency, cost, energy, and carbon trade-offs; (2) an explicit introduction of standard sustainability metrics (PUE, CUE, WUE, energy per token, and carbon emissions per workload) as the quantitative backbone for comparing the surveyed approaches; and (3) a substantially deepened treatment of CES, comparing centralized, distributed, and hybrid algorithmic paradigms, formulating a representative joint scheduling problem, and analyzing feasibility under engineering constraints such as renewable-supply uncertainty, storage limits, grid congestion, workload deadlines, latency requirements, and electricity and carbon price volatility; and (4) a conceptual framework, that links supply-side options, the sustainability-metrics backbone, demand-side workload flexibility, and power-system engineering constraints into a single coordination view. Three research gaps are identified: integrating 24/7 carbon-free electricity procurement with flexible workload scheduling; co-scheduling delay-tolerant training with latency-sensitive inference; and embedding power-system constraints and market signals into CES algorithms. The evidence reviewed indicates that green energy lowers the operational emissions of large-model computing but does not by itself deliver continuous carbon-free operation; green computing techniques can substantially reduce energy per unit of computation at accuracy or latency costs; and CES is a promising coordination paradigm whose large-scale feasibility still needs to be demonstrated under realistic engineering constraints.
- Open Access
- Review
Sustainable and Synergistic Energy Management for Large Model Computing
- Jingbo Hao 1,*,
- Yang Tao 2
Author Information
Received: 18 Jun 2026 | Revised: 18 Sep 2026 | Accepted: 20 Sep 2026 | Published: 10 Oct 2026
Abstract
Keywords
large model | green energy | green computing | computing-energy synergy | data center | sustainability metrics
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