Wafer-Scale Systems: A Carbon Perspective
Golden, Alicia Elgamal, Mariam Mahmoud, Abdulrahman Hills, Gage Wu, Carolejean Wei, Guyeon Brooks, David Michael
Golden, Alicia
Elgamal, Mariam
Mahmoud, Abdulrahman
Hills, Gage
Wu, Carolejean
Wei, Guyeon
Brooks, David Michael
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Department
Computer Science
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Type
Journal article
Date
2025
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Language
English
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Abstract
The rapid rise of Large Language Models (LLMs) has prompted a re-evaluation of system architecture design, making energy efficiency and sustainability more crucial than ever. Recently, wafer-scale architectures have emerged as a viable alternative for LLM training and inference, as evidenced by the success of Cerebras Systems. In this work, we examine the carbon implications of wafer-scale architectures as compared to traditional GPUs. As a case study, we examine LLMs on a Cerebras CS-3 system in order to quantify power and total carbon. Then, we analyze total carbon delay product (tCDP) to evaluate the carbon efficiency and performance potential of these systems. We take the first step towards exploring this trade-off for wafer-scale versus traditional GPU architectures - and ultimately find there exists a rich design space, depending on workload and hardware configuration.
Citation
GoldenAlicia et al., “Wafer-Scale Systems: A Carbon Perspective,” ACM SIGENERGY Energy Informatics Review, vol. 5, no. 2, pp. 118–124, Jul. 2025, doi: 10.1145/3757892.3757909
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ACM SIGENERGY Energy Informatics Review
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Keywords
Wafer-Scale, Carbon Footprint, Large Language Models, Sustainability, Sustainable Computing
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Association for Computing Machinery