Orbital AI Computing: Carbon Tradeoffs Across Satellite Scale
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 enable...
| Authors | Nisha Sarwar, Lei Jiang, Fan Chen |
|---|---|
| Primary category | cs.DC |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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