Predictive Modeling of Energy Consumption in Large Language Model Deployment
Keywords:
large language models, energy prediction, sustainable artificial intelligence, data centers, inference, carbon accounting, governance, robustness, fairness, deployment infrastructureAbstract
Large language models have moved from experimental research artifacts to production infrastructure, and their deployment now carries measurable consequences for electricity demand, carbon emissions, cooling capacity, grid stability, and organizational cost. Predictive modeling of energy consumption in large language model deployment is therefore not merely a technical forecasting exercise but a socio-technical governance mechanism. This paper examines how energy prediction can be framed at the system level, integrating workload characterization, hardware heterogeneity, scheduling policies, data center topology, regional carbon intensity, and service-level requirements. It argues that accurate prediction must account for the distinction between training and inference, the difference between nameplate and real-time power, and the interaction between batching, latency, and utilization. The paper reviews foundational work on machine learning energy accounting, green artificial intelligence, and data center sustainability, then develops a conceptual architecture for predictive energy modeling across cloud, edge, and hybrid deployments. It analyzes structural trade-offs involving accuracy, latency, robustness, fairness, and environmental impact. It also considers governance implications, including reporting standards, procurement, carbon-aware scheduling, and equitable access. The discussion concludes that predictive energy models are most useful when treated as adaptive governance instruments that support transparency, demand response, and long-term infrastructure planning rather than as isolated optimization tools. Such models must remain robust under distribution shift, calibrated under uncertainty, and accountable to both operational and public-interest criteria.
References
1. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 3645–3650). Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1355
2. Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D., Texier, M., & Dean, J. (2021). Carbon emissions and large neural network training. arXiv preprint arXiv:2104.10350.
3. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). Association for Computing Machinery. https://doi.org/10.1145/3442188.3445922
4. Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2020). Green AI. Communications of the ACM, 63(12), 54–63. https://doi.org/10.1145/3381831
5. Luccioni, A. S., Viguier, S., & Ligozat, A.-L. (2023). Estimating the carbon footprint of BLOOM, a 176B parameter language model. Journal of Machine Learning Research, 24(253), 1–15.
6. Wu, C.-J., Raghavendra, R., Gupta, U., Acun, B., Ardalani, N., Maeng, K., Chang, G., Aga, F., Huang, J., Bai, C., Gschwind, M., Gupta, A., Ott, M., Melnikov, A., Candido, S., Brooks, D., Chauhan, G., Lee, B., Lee, H.-H. S., ... Hazelwood, K. (2022). Sustainable AI: Environmental implications, challenges and opportunities. Proceedings of Machine Learning and Systems, 4, 795–813.
7. Dodge, J., Prewitt, T., Tachet des Combes, R., Odmark, E., Schwartz, R., Strubell, E., Luccioni, A. S., & Smith, N. A. (2022). Measuring the carbon intensity of AI in cloud instances. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (pp. 1877–1894). Association for Computing Machinery. https://doi.org/10.1145/3531146.3533234
8. Henderson, P., Hu, J., Romoff, J., Brunskill, E., Jurafsky, D., & Pineau, J. (2020). Towards the systematic reporting of the energy and carbon footprints of machine learning. Journal of Machine Learning Research, 21(248), 1–43.
9. Patterson, D., Gonzalez, J., Hölzle, U., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D. R., Texier, M., & Dean, J. (2022). The carbon footprint of machine learning training will plateau, then shrink. Computer, 55(7), 18–28. https://doi.org/10.1109/MC.2022.3148714
10. Masanet, E., Shehabi, A., Lei, N., Smith, S., & Koomey, J. (2020). Recalibrating global data center energy-use estimates. Science, 367(6481), 984–986. https://doi.org/10.1126/science.aba3758
11. Koomey, J., & Masanet, E. (2021). Does not compute: Avoiding pitfalls in assessing the internet's energy and carbon impacts. Joule, 5(7), 1620–1624. https://doi.org/10.1016/j.joule.2021.06.003
12. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., ... Amodei, D. (2020). Language models are few-shot learners. In Advances in Neural Information Processing Systems (Vol. 33, pp. 1877–1901).
13. Zhang, J., & Gao, H. (2026, March). LLM inference delay prediction based on machine learning algorithms. In 2026 International Conference on Generative Artificial Intelligence and Information Security (GAIIS) (pp. 37-41). IEEE.
14. Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., Schuh, P., Shi, K., Tsvyashchenko, S., Maynez, J., Rao, A., Barnes, P., Tay, Y., Shazeer, N., Prabhakaran, V., ... Fiedel, N. (2022). PaLM: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311.
15. Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., & Lample, G. (2023). LLaMA: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.
16. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J. Q., Demszky, D., ... Liang, P. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
17. Weidinger, L., Uesato, J., Rauh, M., Griffin, C., Huang, P.-S., Mellor, J., Glaese, A., Cheng, M., Balle, B., Kasirzadeh, A., Biles, C., Brown, S., Kenton, Z., Hawkins, W., Stepleton, T., Birhane, A., Hendricks, L. A., Rimell, L., Isaac, W., ... Gabriel, I. (2021). Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359.
18. Sallam, M. (2023). ChatGPT utility in healthcare education, research, and practice: Systematic review on the promising perspectives and valid concerns. Healthcare, 11(6), 887. https://doi.org/10.3390/healthcare11060887
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Data Intelligence and AI Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
