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Characterizing Power Management Opportunities for LLMs in the Cloud
Abstract
Recent innovation in large language models (LLMs), and their myriad use cases have rapidly driven up the compute demand for datacenter GPUs. Several cloud providers and other enterprises plan to substantially grow their datacenter capacity to support these new workloads. A key bottleneck resource in datacenters is power, which LLMs are quickly saturating due to their rapidly increasing model sizes.
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“Nonetheless, [38] only considers the token generation phase, while neglecting the prompt processing phase (i.e., processing user prompts to generate the first output token) which is also energy-intensive [32, 33, 45].”
From A Water Efficiency Dataset for African Data Centers (2025) · cites this paper as [32]
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On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
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“Cloud providers and datacenter operators today face a massive GPU capacity crunch due to the explosion in demand for large language models (LLMs) [8].”
BackgroundEfficient Memory Management for Large Language Model Serving with PagedAttention
Kwon, W., Li, Z., Zhuang, S., Sheng, Y., Zheng, L., Yu, C. H., Gonzalez, J., Zhang, H., & Stoica, I. (2023) · Proceedings of the 29th Symposium on Operating Systems Principles · doi:10.1145/3600006.3613165 · Cited by 1,817 · cited 1×
“For LLM inference, we use DeepSpeed Inference [4, 39] and vLLM [32], which support our target models.”
MethodEfficient Memory Management for Large Language Model Serving with PagedAttention
Kwon, W., Li, Z., Zhuang, S., Sheng, Y., Zheng, L., Yu, C. H., Gonzalez, J. E., Zhang, H., & Stoica, I. (2023) · arXiv · doi:10.48550/arxiv.2309.06180 · Cited by 1,802
Scaling Instruction-Finetuned Language Models
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, Y., Wang, X., Dehghani, M., Brahma, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2210.11416 · Cited by 1,441
The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
Patterson, D., Gonzalez, J., Holzle, U., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D. R., Texier, M., & Dean, J. (2022) · Computer · doi:10.1109/mc.2022.3148714 · Cited by 400
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Cited by
Sorted by citations
Splitwise: Efficient Generative LLM Inference Using Phase Splitting
Patel, P., Choukse, E., Zhang, C., Shah, A., Goiri, Í., Maleki, S., & Bianchini, R. (2024) · 2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA) · doi:10.1109/isca59077.2024.00019 · Cited by 298
“Finally, power optimization is attractive for a CSP, since it enables more GPUs to be deployed in the same datacenter [62], [63], but it may not be as important to the user.”
BackgroundMethodSelf-citationDynamoLLM: Designing LLM Inference Clusters for Performance and Energy Efficiency
Stojkovic, J., Zhang, C., Goiri, Í., Torrellas, J., & Choukse, E. (2025) · 2025 IEEE International Symposium on High Performance Computer Architecture (HPCA) · doi:10.1109/hpca61900.2025.00102 · Cited by 112
Self-citationXiao, T., Nerini, F. F., Matthews, H. D., Tavoni, M., & You, F. (2025) · Nature Sustainability · doi:10.1038/s41893-025-01681-y · Cited by 121
A Water Efficiency Dataset for African Data Centers
Shumba, N., Tshekiso, O., Li, P., Fanti, G., & Ren, S. (2025) · Proceedings of the ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies · doi:10.1145/3715335.3735483 · Cited by 10
Metadata
- Authors
- Patel, P., Choukse, E., Zhang, C., Goiri, Í., Warrier, B., Mahalingam, N., & Bianchini, R.
- Affiliations
- Microsoft Azure, Redmond, USA
- Microsoft Azure, Redmond, United States of America
- ORCID
- Venue
- Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3
- Publisher
- ACM
- Published
- 2024-04-27
- Pages
- 207-222
- Cited by
- 100
- References
- 9
- Funders
- National Science Foundation (CNS-2104548)
- VMware
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