Great Power, Great Responsibility: Recommendations for Reducing Energy for Training Language Models
Open accessAbstract
The energy requirements of current natural language processing models continue to grow at a rapid, unsustainable pace.Recent works highlighting this problem conclude there is an urgent need for methods that reduce the energy needs of NLP and machine learning more broadly.In this article, we investigate techniques that can be used to reduce the energy consumption of common NLP applications.In particular, we focus on techniques to measure energy usage and different hardware and datacenter-oriented settings that can be tuned to reduce energy consumption for training and inference for language models.We characterize the impact of these settings on metrics such as computational performance and energy consumption through experiments conducted on a high performance computing system as well as popular cloud computing platforms.These techniques can lead to significant reduction in energy consumption when training language models or their use for inference.For example, power-capping, which limits the maximum power a GPU can consume, can enable a 15% decrease in energy usage with marginal increase in overall computation time when training a transformer-based language model. 1
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Broader research indicates extensive computational resources are needed to train and deploy advanced AI models.
“broader research on the extensive computational resources needed to train and deploy advanced AI models [53, 78]”
From Understanding the Practices, Perceptions, and (Dis)Trust of Generative AI among Instructors: A Mixed-methods Study in the U.S. Higher Education (2025) · cites this paper as [78]
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Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204 · see citing works
Pointer Sentinel Mixture ModelsPreprint
Merity, S., Xiong, C., Bradbury, J., & Socher, R. (2016) · International Conference on Learning Representations · doi:10.48550/arxiv.1609.07843 · Cited by 5,051 · see citing works
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019) · arXiv · doi:10.48550/arxiv.1910.01108 · Cited by 4,653 · see citing works
Energy and Policy Considerations for Deep Learning in NLP
Strubell, E., Ganesh, A., & McCallum, A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1355 · Cited by 3,684 · see citing works
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Frankle, J., & Carbin, M. (2018) · arXiv · doi:10.48550/arxiv.1803.03635 · Cited by 1,529 · see citing works
Big Bird: Transformers for Longer Sequences
Zaheer, M., Guruganesh, G., Dubey, A., Ainslie, J., Alberti, C., Ontanon, S., Pham, P., Ravula, A., Wang, Q., Yang, L., et al. (2020) · arXiv · doi:10.48550/arxiv.2007.14062 · Cited by 1,349 · see citing works
An Analysis of Deep Neural Network Models for Practical Applications
Canziani, A., Paszke, A., & Culurciello, E. (2016) · arXiv · doi:10.48550/arxiv.1605.07678 · Cited by 969 · see citing works
The Computational Limits of Deep Learning
Thompson, N., Greenewald, K. H., Lee, K., & Manso, G. F. (2023) · Ninth Computing within Limits 2023 · doi:10.21428/bf6fb269.1f033948 · Cited by 737 · see citing works
The Computational Limits of Deep Learning
Thompson, N. C., Greenewald, K., Lee, K., & Manso, G. F. (2020) · arXiv · doi:10.48550/arxiv.2007.05558 · Cited by 538 · see citing works
Carbon Emissions and Large Neural Network Training
Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D., Texier, M., & Dean, J. (2021) · arXiv · doi:10.48550/arxiv.2104.10350 · Cited by 486 · see citing works
Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning
Henderson, P., Hu, J., Romoff, J., Brunskill, E., Jurafsky, D., & Pineau, J. (2020) · arXiv · doi:10.48550/arxiv.2002.05651 · Cited by 393 · see citing works
Interactive Supercomputing on 40,000 Cores for Machine Learning and Data Analysis
Reuther, A., Kepner, J., Byun, C., Samsi, S., Arcand, W., Bestor, D., Bergeron, B., Gadepally, V. N., Houle, M., Hubbell, M., et al. (2018) · IEEE High Performance Extreme Computing Conference · doi:10.1109/hpec.2018.8547629 · Cited by 251 · see citing works
Self-citationShow Your Work: Improved Reporting of Experimental Results
Dodge, J., Gururangan, S., Card, D., Schwartz, R., & Smith, N. A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1224 · Cited by 233 · see citing works
Investigating power capping toward energy‐efficient scientific applications
Haidar, A., Jagode, H., Vaccaro, P., YarKhan, A., Tomov, S. Z., & Dongarra, J. J. (2018) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.4485 · Cited by 46 · see citing works
Samsi, S., Weiss, M. L., Bestor, D., Li, B., Jones, M. P., Reuther, A., Edelman, D., Arcand, W., Byun, C., Holodnak, J. T., et al. (2021) · IEEE High Performance Extreme Computing Conference · doi:10.1109/hpec49654.2021.9622850 · Cited by 41 · see citing works
Self-citationBenchmarking Resource Usage for Efficient Distributed Deep Learning
Frey, N. C., Li, B., McDonald, J., Zhao, D., Jones, M. P., Bestor, D., Tiwari, D., Gadepally, V. N., & Samsi, S. (2022) · IEEE High Performance Extreme Computing Conference · doi:10.1109/hpec55821.2022.9926375 · Cited by 14 · see citing works
Self-citationImproved Compression for Word Embeddings by Scaling Principal Components
McDonald, J., Samsi, S., Edelman, D., Byun, C., Kepner, J., & Gadepally, V. N. (2021) · IEEE High Performance Extreme Computing Conference · doi:10.1109/hpec49654.2021.9622825 · Cited by 1 · see citing works
Self-citationBERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805
Flagged first, then by citations
Research works printed but not linked: 4
- Jonathan Frankle and Michael Carbin. 2019. The lot- tery ticket hypothesis: Finding sparse, trainable neu- ral networks. In International Conference on Learn- ing Representations.
- Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2017. Pointer sentinel mixture mod- els. In Proceedings of the 5th International Confer- ence on Learning Representations (ICLR).
- Jiaqi Mu and Pramod Viswanath. 2018. All-but-the- top: Simple and effective postprocessing for word representations. In International Conference on Learning Representations.
- Roy Schwartz, Jesse Dodge, Noah Smith, and Oren Et- zioni. 2020. Green ai. Communications of the ACM, 63:54 -63.
Cited by
Sorted by citations
Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning
Desislavov, R., Martínez-Plumed, F., & Hernández-Orallo, J. (2023) · Sustainable Computing: Informatics and Systems · doi:10.1016/j.suscom.2023.100857 · Cited by 269 · see citing works
Zhou, H., Hu, C., Yuan, Y., Cui, Y., Jin, Y., Chen, C., Wu, H., Yuan, D., Jiang, L., Wu, D., et al. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3465447 · Cited by 277 · see citing works
From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference
Samsi, S., Zhao, D., McDonald, J., Li, B., Michaleas, A., Jones, M., Bergeron, W., Kepner, J., Tiwari, D., & Gadepally, V. (2023) · 2023 IEEE High Performance Extreme Computing Conference (HPEC) · doi:10.1109/hpec58863.2023.10363447 · Cited by 225 · see citing works
Self-citationMeasuring and Improving the Energy Efficiency of Large Language Models Inference
Argerich, M. F., & Patiño-Martı́nez, M. (2024) · IEEE Access · doi:10.1109/access.2024.3409745 · Cited by 89 · see citing works
Photonic Neural Networks Based on Integrated Silicon Microresonators
Biasi, S., Donati, G. P., Lugnan, A., Mancinelli, M., Staffoli, E., & Pavesi, L. (2023) · Intelligent Computing · doi:10.34133/icomputing.0067 · Cited by 67 · see citing works
Lyu, W., Zhang, S., Chung, T., Sun, Y., & Zhang, Y. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100383 · Cited by 68 · see citing works
“These findings align with broader research on the extensive computational resources needed to train and deploy advanced AI models [53, 78].”
Krzywaniak, A., Czarnul, P., & Proficz, J. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.03.041 · Cited by 45 · see citing works
EvoPrompting: Language Models for Code-Level Neural Architecture Search
Chen, A., Dohan, D. M., & So, D. R. (2023) · arXiv · doi:10.48550/arxiv.2302.14838 · Cited by 45 · see citing works
Bikkasani, D. C. (2024) · AI and Ethics · doi:10.1007/s43681-024-00642-z · Cited by 37 · see citing works
Soto-Díaz, R., Vásquez‐Carbonell, M., & Escorcia‐Gutierrez, J. (2025) · Engineering Science and Technology an International Journal · doi:10.1016/j.jestch.2025.101949 · Cited by 30 · see citing works
Environmental impact of large language models in medicine
Kleinig, O., Sinhal, S., Khurram, R., Gao, C., Spajic, L., Zannettino, A. C. W., Schnitzler, M., Guo, C. W., Zaman, S. J., Smallbone, H., et al. (2024) · Internal Medicine Journal · doi:10.1111/imj.16549 · Cited by 20 · see citing works
A Comprehensive Review on the Application of Large Language Models (LLMs) in Power Systems
Ghafari, M., Sami, A., Rezapour, H., Ghayour, S. S., Lin, F., Başaran, K., Lăzăroiu, G. C., & Siano, P. (2025) · IEEE Access · doi:10.1109/access.2025.3637226 · Cited by 20 · see citing works
Wang, H., Rajakumar, V. S., Golec, M., Gill, S. S., & Uhlig, S. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-06984-7 · Cited by 19 · see citing works
Solórzano, A. L. V., Sato, K., Yamamoto, K., Shoji, F., Brandt, J. M., Schwaller, B., Walton, S. P., Green, J., & Tiwari, D. (2024) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc41406.2024.00030 · Cited by 17 · see citing works
Self-citationRethinking chemical research in the age of large language models
MacKnight, R., Boiko, D. A., Regio, J. E., Gallegos, L. C., Neukomm, T. A., & Gomes, G. (2025) · Nature Computational Science · doi:10.1038/s43588-025-00811-y · Cited by 17 · see citing works
LLMs on a Budget: System-Level Approaches to Power-Efficient and Scalable Fine-Tuning
Gogineni, K., Suvizi, A., & Venkataramani, G. (2025) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2025.3580498 · Cited by 14 · see citing works
Progress and challenges of phase change memory for in-memory computing
Su, X., Guo, Z., Zhang, G., Tsai, H.-Y. S., & Li, N. (2025) · Current Opinion in Solid State and Materials Science · doi:10.1016/j.cossms.2025.101225 · Cited by 13 · see citing works
A text-based recommender system for recommending relevant news articles
Walek, B., & Müller, P. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125816 · Cited by 11 · see citing works
Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy
Alizadeh, N., Belchev, B., Saurabh, N., Kelbert, P., & Castor, F. (2025) · International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr66628.2025.00109 · Cited by 10 · see citing works
AI Analytics for Carbon-Neutral City Planning: A Systematic Review of Applications
Cong, C., Page, J., Kwak, Y., Deal, B. M., & Kalantari, Z. (2024) · Urban Science · doi:10.3390/urbansci8030104 · Cited by 9 · see citing works
Impact of decoding strategies on GPU energy usage in large language model text generation
Nik, A., Riegler, M. A., & Halvorsen, P. (2025) · Scientific Reports · doi:10.1038/s41598-025-31896-0 · Cited by 11 · see citing works
LLMs beyond the lab: the ethics and epistemics of real-world AI research
Mollen, J. (2024) · Ethics and Information Technology · doi:10.1007/s10676-024-09819-w · Cited by 9 · see citing works
Environmental law's principles applied to artificial intelligence: a path towards regulation
Goff, T. L. (2025) · International Review of Law Computers & Technology · doi:10.1080/13600869.2025.2506166 · Cited by 7 · see citing works
A scoping review of AI for sustainability and sustainable AI in higher education
Daniel, B. K., Podgorodnichenko, N., & Carr, S. (2026) · Discover Computing · doi:10.1007/s10791-025-09861-2 · Cited by 12 · see citing works
An exploration of prompting LLMs to generate energy-efficient code
Cappendijk, T., de Reus, P., & Oprescu, A. (2025) · IEEE/ACM International Workshop on Green and Sustainable Software (GREENS) · doi:10.1109/greens66463.2025.00010 · Cited by 7 · see citing works
Lower Energy Large Language Models (LLMs)
Lin, H.-Y., & Voas, J. M. (2023) · Computer · doi:10.1109/mc.2023.3278160 · Cited by 6 · see citing works
Carbon-Aware Workload Shifting for Mitigating Environmental Impact of Generative AI Models
Zhang, E., Wu, D. D., & Boman, J. (2024) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics62450.2024.00087 · Cited by 6 · see citing works
Evaluating and Comparing the Potentials in Primary Response for GPU and CPU Data Centers
Zhou, Y., Paredes, Á., Essayeh, C., & Morstyn, T. (2024) · IEEE Power Engineering Society General Meeting · doi:10.1109/pesgm51994.2024.10689061 · Cited by 6 · see citing works
Habib, F., Ali, Z., Azam, A., Kamran, K., & Pasha, F. M. (2024) · Frontiers in Big Data · doi:10.3389/fdata.2024.1387325 · Cited by 5 · see citing works
Koszczał, G., Dobrosolski, J., Matuszek, M. R., & Czarnul, P. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-48803-0_1 · Cited by 4 · see citing works
Scheduling Machine Learning Compressible Inference Tasks with Limited Energy Budget
Barros, T. D. S., Ferré, D., Giroire, F., Aparicio-Pardo, R., & Pérennès, S. (2024) · Proceedings of the International Conference on Parallel Processing · doi:10.1145/3673038.3673106 · Cited by 4 · see citing works
Maximizing Power-Constrained Supercomputing Throughput
Ding, N., Antepara, O., Zhao, Z., Austin, B., Oliker, L., Wright, N. J., & Williams, S. (2025) · ISC High Performance (ISC) · doi:10.23919/isc.2025.11017728 · Cited by 3 · see citing works
Energy Efficient Scheduling of AI/ML Workloads on Multi Instance Gpus with Dynamic Repartitioning
Lipe, E., Karia, N., Espenshade, C., Stein, C., Tantawi, A. N., & Tardieu, O. (2025) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid64434.2025.00066 · Cited by 3 · see citing works
Wang, H., Gill, S. S., & Uhlig, S. (2025) · IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW) · doi:10.1109/icdcsw63273.2025.00106 · Cited by 3 · see citing works
Reducing Energy Footprint of LLM Inference Through FPGA-Based Heterogeneous Computing Platforms
Monteiro, T. C., & Guerrieri, A. (2026) · Electronics · doi:10.3390/electronics15051052 · Cited by 2 · see citing works
Bigeng, 陈. C., Ke, 李. L., Yiru, 赵. Z., & Yu, S. (2024) · Laser & Optoelectronics Progress · doi:10.3788/lop241594 · Cited by 2 · see citing works
Ainet0: AI Forecasting Based Carbon Neutral Cloud Resource Management for Net Zero Targets
Wang, H., Kannan, S., Gill, S. S., & Uhlig, S. (2025) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.70166 · Cited by 2 · see citing works
Theorizing sustainable, low-resource MT in development settings
Riemland, M. (2023) · Translation Spaces · doi:10.1075/ts.22018.rie · Cited by 2 · see citing works
Sustainable Engineering of Machine Learning-Enabled Systems: A Systematic Mapping StudyPreprint
Chadli, K., Botterweck, G., & Saber, T. (2024) · Research Square · doi:10.21203/rs.3.rs-4694122/v1 · Cited by 2 · see citing works
Comparison of basic recommendation methods for Czech news articles
Müller, P., & Walek, B. (2024) · IEEE International Scientific Conference on Informatics (Informatics) · doi:10.1109/informatics62280.2024.10900839 · Cited by 1 · see citing works
The Efficiency Frontier: Optimal Combinations of Energy-Saving Strategies in Data Centers
Mokri, A., & Gadepally, V. N. (2025) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai64502.2025.00058 · Cited by 1 · see citing works
Self-citationAdaptive GPU Power Capping: Balancing Energy Efficiency,Thermal Control and Performance
Desai, T., Shah, J., Alavani, G., Saha, S., & Sarkar, S. (2025) · ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC) · doi:10.1145/3731545.3735119 · Cited by 1 · see citing works
Yao, Y., Zhu, B., Xiao, Y., & Liu, H. (2025) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2025.101220 · Cited by 1 · see citing works
Methods for Setting Device Specifications for Analog In‐Memory Computing Inference
Wu, Z., Su, X., Rasch, M. J., & Li, N. (2026) · Advanced Intelligent Systems · doi:10.1002/aisy.202501355 · Cited by 1 · see citing works
Diakun, O., Dobrosolski, J., & Czarnul, P. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-71115-2_23 · Cited by 1 · see citing works
Trade-Off between Performance and Power Consumption in Deep Learning Training
Yao, Y., & Xiao, Y. (2024) · International Symposium on Computer Engineering and Intelligent Communications (ISCEIC) · doi:10.1109/isceic63613.2024.10810178 · Cited by 1 · see citing works
DASH: Scheduling Deep Learning Workloads on Multi-Generational GPU-Accelerated Clusters
Li, B., Patel, T., Gadepally, V. N., Gettings, K., Samsi, S., & Tiwari, D. (2022) · IEEE High Performance Extreme Computing Conference · doi:10.1109/hpec55821.2022.9926390 · Cited by 2 · see citing works
Self-citation(2023) · Computer · doi:10.1109/mc.2023.3308281 · Cited by 1 · see citing works
Tutorial: Creating AI-Generated Images for Vignette Studies in Python or RPreprint
Arlinghaus, C. S., & Maier, G. W. (2024) · doi:10.31219/osf.io/a56dy · Cited by 1 · see citing works
AI-Driven Sustainability in the Energy Sector
Goff, T. L. (2025) · doi:10.1093/9780198945215.003.0027 · Cited by 1 · see citing works
An Analysis of Energy Requirement for Computer Vision Algorithms
Edelman, D., Samsi, S., McDonald, J., Michaleas, A. M., & Gadepally, V. N. (2023) · IEEE High Performance Extreme Computing Conference · doi:10.1109/hpec58863.2023.10363596
Self-citationRESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability
Sangarya, V., & Kim, J.-E. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i19.34232
Building Green Generative AI: An Ecosystem-Wide Approach to Environmental Sustainability
Helms, F., Hönemann, K., & Wiesche, M. (2025) · Bled eConference · doi:10.18690/um.fov.4.2025.38
Evans, S. C., Ekanayake, S. P., Duncan, A., Rose, B., Huang, H., Ndiaye, I., & Dahlin, N. (2025) · IEEE Conference on Technologies for Sustainability · doi:10.1109/sustech63138.2025.11025705
Spicing Up LLMs: The Role of PAPRICA Pruning in Linear Computation Coding
Fröhlich, J. S., Rosenberger, H., & Müller, R. R. (2025) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai64502.2025.00064
Du, Y., Wang, X., Guo, N., Yu, Z., Yang, Q., Zhang, K., Hsieh, M.-H., Rebentrost, P., & Tao, D. (2025) · Springer eBooks · doi:10.1007/978-981-95-1284-3_5
Time Series Models for Predicting Application GPU Utilization and Power Draw
Coleman, E., Parry, D. X., & Sosonkina, M. (2025) · Journal of Computer and Communications · doi:10.4236/jcc.2025.1312004
Samanta, A., Poyekar, S., Dutta, A. K., & Adhya, A. (2025) · International Telecommunication Networks and Applications Conference (ITNAC) · doi:10.1109/itnac66378.2025.11302513
Wang, F., Liang, X., & Du, X. (2026) · Sensors · doi:10.3390/s26020568
Leveraging Large Language Models (LLMs) for Service Optimization in Beyond 5G (B5G) Networks
Muralidharan, S., Woo, W., & Lee, N. (2025) · Springer eBooks · doi:10.1007/978-3-032-06418-9_15
Nelapatla, P. R., Starkey, E., & Zhou, Y. X. (2026) · Electronics · doi:10.3390/electronics15194473
Amiri, M., Mokari, A., & Bocklitz, T. W. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02369-8
The Nexus of AI, Energy, and Climate
Jlassi, I. (2026) · IGI Global eBooks · doi:10.4018/979-8-2600-2025-8.ch009
- Authors
- McDonald, J., Li, B., Frey, N., Tiwari, D., Gadepally, V., & Samsi, S.
- Affiliations
- MIT Lincoln Laboratory (US)
- Northeastern University (US)
- Venue
- Findings of the Association for Computational Linguistics: NAACL 2022
- Published
- 2022
- Pages
- 1962-1970
- OpenAlex
- W4287889735
- Cited by
- 62
- References
- 18
- Citations by year
- 2026: 7 · 2025: 27 · 2024: 19 · 2023: 8 · 2022: 1
- FWCI
- 22.24
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: NAACL (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1910601, 1920020, 2124897)
- Leukemia and Lymphoma Society of Canada
- Massachusetts Green High Performance Computing Center
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Keywords
- energy consumption, language model training, energy efficiency, power capping, cloud computing, transformer models
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