Green AI: Energy-Efficient Large Language Models for Sustainable Artificial Intelligence

In conclusion, Green AI should be regarded not only as an environmental initiative but also as a strategic approach to improving scalability, affordability, and long-term sustainability. As AI technologies continue to evolve, adopting sustainable development practices will be essential for ensuring that artificial intelligence delivers societal benefits while minimizing its ecological impact. Researchers, industry leaders, and policymakers must work together to make sustainable AI development a central objective for the future.

[1]  A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention Is All You Need,” in Proceedings of the 31st International Conference on Neural Information Processing Systems (NeurIPS), 2017, pp. 5998–6008. [2]  T. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al., “Language Models are Few-Shot Learners,” in Advances in Neural Information Processing Systems (NeurIPS), vol. 33, pp. 1877–1901, 2020. [3]  R. Bommasani, D. Hudson, E. Adeli, R. Altman, S. Arora, S. von Arx, M. Bernstein, J. Bohg, A. Bosselut, E. Brunskill, et al., On the Opportunities and Risks of Foundation Models, Stanford University, Center for Research on Foundation Models (CRFM), 2021. [4]  I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016. [5]   S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Upper Saddle River, NJ, USA: Pearson Education, 2021. [6]  R. Schwartz, J. Dodge, N. A. Smith, and O. Etzioni, “Green AI,” Communications of the ACM, vol. 63, no. 12, pp. 54–63, Dec. 2020. [7]   E. Strubell, A. Ganesh, and A. McCallum, “Energy and Policy Considerations for Deep Learning in NLP,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), Florence, Italy, 2019, pp. 3645–3650. [8]  D. Patterson, J. Gonzalez, Q. Le, C. Liang, L. Munguia, D. Rothchild, D. So, M. Texier, and J. Dean, “Carbon Emissions and Large Neural Network Training,” arXiv preprint arXiv:2104.10350, 2021. [9]   G. Hinton, O. Vinyals, and J. Dean, “Distilling the Knowledge in a Neural Network,” arXiv preprint arXiv:1503.02531, 2015. [10]  S. Han, H. Mao, and W. J. Dally, “Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding,” in International Conference on Learning Representations (ICLR), 2016.