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Embodied Emissions of 2.5D and 3D Scaling in Chipletized GPUs for Low-Carbon AI
Hannah Watson, Arjun Singh, Jennifer Stephenson, Job Soethoudt, Jonas Svedas, Debjyoti Bhattacharjee, James Myers

Embodied Emissions of 2.5D and 3D Scaling in Chipletized GPUs for Low-Carbon AI

Computer Science & Mathematics Published 2026 Verification Pending

Overview & Synopsis

GPU compute resources are scaling to support growing AI workloads, leading to further rises in the climate change impact of hyperscale data centers that rely on high-performance GPUs. To model how packaging decisions impact the carbon footprint we have estimated the embodied emissions (EE) of a chiplet-based GPU that has been scaled in 2.5D and 3D using the imec.netzero modelling platform. We doubled the number of compute units and high bandwidth memory (HBM) capacity in the 2.5D and 3D scaled systems and found that 3D scaling results in an 89% increase in EE, as compared to an increase of 141% for 2.5D scaling, positioning 3D scaling as an attractive solution to limit the embodied emissions due to GPU scaling.

Indexed from repository: arXiv (arxiv.org)
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Document Information

Document Title Embodied Emissions of 2.5D and 3D Scaling in Chipletized GPUs for Low-Carbon AI
Author / Organization Hannah Watson, Arjun Singh, Jennifer Stephenson, Job Soethoudt, Jonas Svedas, Debjyoti Bhattacharjee, James Myers
Subject & Discipline cs.AR
Categories Computer Science & Mathematics
Publication Year 2026
License / Rights arXiv Open Access / Creative Commons
Resource Verification Listed PDF (Verification Pending)
Original Host Website arXiv (arxiv.org)
File Format application/pdf (Portable Document Format)
Language EN
Catalog ID DOC-118403
Original Source Link Visit Source Page on arxiv.org »