Toward Sustainable HPC: Carbon Footprint Estimation and Environmental Implications of HPC Systems
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Abstract (arXiv)
The rapid growth in demand for HPC systems has led to a rise in carbon footprint, which requires urgent intervention. In this work, we present a comprehensive analysis of the carbon footprint of high-performance computing (HPC) systems, considering the carbon footprint during both the hardware manufacturing and system operational stages. Our work employs HPC hardware component carbon footprint modeling, regional carbon intensity analysis, and experimental characterization of the system life cycle to highlight the importance of quantifying the carbon footprint of HPC systems.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Jun 2023.
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Citing papers per year: 2023: 1; 2024: 4; 2025: 5; 2026: 2.
12 papers.
- 2024Toward Sustainable GenAI using Generation Directives for Carbon-Friendly Large Language Model Inferencecited by 13
- 2024Towards Sustainable Large Language Model Servingcited by 12
- 2025Unveiling Environmental Impacts of Large Language Model Serving: A Functional Unit Viewcited by 11
- 2024A Digital Twin Framework for Liquid-cooled Supercomputers as Demonstrated at Exascalecited by 3
- 2024Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunitiescited by 3
- 2025Energy-Conscious LLM Decoding: Impact of Text Generation Strategies on GPU Energy Consumptioncited by 1
- 2025Advancing AI Challenges for the United States Department of the Air Forcecited by 1
- 2026BIRDS: Characterizing and Understanding Biodiversity Impact of Large Language Model Servingcited by 1
- 2023A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architecturescited by 0
- 2025AOLO: Analysis and Optimization For Low-Carbon Oriented Wireless Large Language Model Servicescited by 0
- 2025Lifetime-Aware Design for Item-Level Intelligence at the Extreme Edgecited by 0
- 2026DumpsterCluster: From Dumpster Diving to Serving LLaMA-70B on 60 GPUscited by 0
- 2026DumpsterCluster: From Dumpster Diving to Serving LLaMA-70B on 60 GPUs10 Jul 2026
- 2026BIRDS: Characterizing and Understanding Biodiversity Impact of Large Language Model Serving26 May 2026
- 2025Advancing AI Challenges for the United States Department of the Air Force31 Oct 2025
- 2025Lifetime-Aware Design for Item-Level Intelligence at the Extreme Edge9 Sep 2025
- 2025AOLO: Analysis and Optimization For Low-Carbon Oriented Wireless Large Language Model Services6 Mar 2025
- 2025Energy-Conscious LLM Decoding: Impact of Text Generation Strategies on GPU Energy Consumption17 Feb 2025
- 2025Unveiling Environmental Impacts of Large Language Model Serving: A Functional Unit View16 Feb 2025
- 2024Towards Sustainable Large Language Model Serving31 Dec 2024
- 2024Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunities11 Oct 2024
- 2024A Digital Twin Framework for Liquid-cooled Supercomputers as Demonstrated at Exascale7 Oct 2024
- 2024Toward Sustainable GenAI using Generation Directives for Carbon-Friendly Large Language Model Inference19 Mar 2024
- 2023A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures29 Nov 2023