Work

When machine learning models retire, decay, or become obsolete: A review on algorithms, software, and hardware

Year: 2025

Type: article

Abstract: The exponential growth of artificial intelligence (AI) has raised serious concerns about its environmental impact, with large-scale AI models now consuming substantial amounts of energy. From this lens, this review systematically examines the current landscape of energy consumption in AI systems across their lifecycle, from training to deployment and inference. This review also introduces a holistic framework for assessing and improving AI systems through six interconnected dimensions: grid-optimized scheduling, resource-efficient architectures, energy-aware algorithms, environmental impact metrics, algorithmic optimization, and adaptive computation. Furthermore, we evaluate the trade-offs between model performance and environmental impact and propose methodological standards for measuring and reporting energy consumption in AI research and development. The paper concludes with actionable best practices to foster a more sustainable AI ecosystem and identifies promising directions and persistent challenges in reducing AI's carbon footprint.

Source: Renewable and Sustainable Energy Reviews

DOI: 10.1016/j.rser.2025.116231

Author: M.Z. Naser

Institution: Clemson University

Language: English

Topic: Green IT and Sustainability

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Climate action, Affordable and clean energy, Responsible consumption and production

Open Access status: hybrid

Estimated APC paid: $5,220