Green AI: Energy-Efficient Training of Large-Scale Models
DOI:
https://doi.org/10.15662/IJRAI.2025.0802001Keywords:
Green AI, Energy-Efficient Training, Sustainable AI, Model Compression, Neural Architecture Search, Carbon Footprint, Efficient Deep Learning, Pruning, Quantization, Environmental ImpactAbstract
The rapid advancement of artificial intelligence (AI) and deep learning has led to unprecedented performance gains across various domains. However, the exponential growth in model size and computational complexity has resulted in significant energy consumption and carbon emissions. The concept of Green AI emphasizes the need for energy-efficient, environmentally sustainable AI practices that reduce the ecological footprint of training and deploying large-scale models. This paper explores the core principles, methodologies, and trade-offs involved in implementing Green AI, with a specific focus on energy-efficient training of large-scale models such as transformers, BERT, and GPT. It highlights techniques such as model pruning, quantization, knowledge distillation, efficient neural architectures (e.g., MobileNets, EfficientNet), and hardware-aware neural architecture search (NAS). We propose a structured methodology for evaluating energy efficiency in model training, incorporating metrics like FLOPs, energy-to-accuracy ratio (EAR), and carbon footprint per training run. Experiments demonstrate how applying Green AI techniques can lead to significant reductions in energy usage—up to 60%—with minimal compromise on model accuracy. The study also presents a comparative analysis between baseline large models and their optimized, green counterparts in natural language processing and computer vision tasks. A trade-off between performance and sustainability is discussed, emphasizing that Green AI not only serves ecological goals but also economic and accessibility objectives— especially for researchers and organizations with limited computational resources. Key challenges include the lack of standardized evaluation protocols, limited availability of energy tracking tools, and the tendency of research communities to prioritize performance over efficiency. The paper concludes with directions for future research, including the development of greener benchmarks, transparent reporting practices, and AI regulatory frameworks promoting sustainability.
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