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This research quantifies the lifecycle carbon intensity of Low Earth Orbit AI systems, demonstrating how high-performance accelerators like the DGX H100 effectively amortize fixed launch emissions compared to lightweight alternatives. The study establishes that hardware selection critically dictates the space-ground tradeoff for sustainable orbital computing infrastructure.
Orbital AI Computing: Carbon Tradeoffs Across Satellite Scale
arXiv:2608.14557v1 Announce Type: cross Abstract: Low Earth Orbit (LEO) computing is emerging for low-latency, globally distributed AI services, enabled by advances in satellite constellations and reusable launch systems. However, its sustainability remains unclear. Prior work introduces ESpaS, a framework for estimating lifecycle carbon intensity, but models systems using generic datacenter configurations and does not capture modern AI hardware, where power, mass, and compute characteristics vary widely and launch emissions scale with system mass. In this work, we extend ESpaS with accelerator-aware modeling and evaluate two representative systems: a lightweight Jetson AGX Orin for small satellites and a high-performance DGX H100 enabled by large-payload launch platforms. We show that launch emissions act as a fixed carbon overhead: low-mass systems minimize absolute emissions, while high-performance systems amortize this cost more effectively, reducing carbon intensity. Consequently, the space-ground tradeoff is highly sensitive to hardware choice, highlighting the need for accelerator-aware baselines in orbital AI computing.
Abstract:Low Earth Orbit (LEO) computing is emerging for low-latency, globally distributed AI services, enabled by advances in satellite constellations and reusable launch systems. However, its sustainability remains unclear. Prior work introduces ESpaS, a framework for estimating lifecycle carbon intensity, but models systems using generic datacenter configurations and does not capture modern AI hardware, where power, mass, and compute characteristics vary widely and launch emissions scale with system mass. In this work, we extend ESpaS with accelerator-aware modeling and evaluate two representative systems: a lightweight Jetson AGX Orin for small satellites and a high-performance DGX H100 enabled by large-payload launch platforms. We show that launch emissions act as a fixed carbon overhead: low-mass systems minimize absolute emissions, while high-performance systems amortize this cost more effectively, reducing carbon intensity. Consequently, the space-ground tradeoff is highly sensitive to hardware choice, highlighting the need for accelerator-aware baselines in orbital AI computing.
Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
arXiv:2608.14557 [cs.DC]
(or arXiv:2608.14557v1 [cs.DC] for this version)
https://doi.org/10.48550/arXiv.2608.14557 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1145/3787109.3815323 Focus to learn more DOI(s) linking to related resources
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