发表机构
Indiana University Bloomington（印第安纳大学伯明顿分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究扩展ESpaS框架以支持加速器感知建模，对比评估Jetson AGX Orin与DGX H100系统，发现发射排放为固定碳开销，高性能系统分摊成本后碳强度更低，凸显轨道AI计算需加速器感知基线。
AI 中文摘要
低地球轨道（LEO）计算正随着卫星星座和可重复使用运载系统的发展而兴起，用于实现低延迟、全球分布式的AI服务，但其可持续性仍不明确。此前研究推出了ESpaS这一生命周期碳强度估算框架，但该框架采用通用数据中心配置对系统进行建模，未考虑现代AI硬件的情况——现代AI硬件的功耗、质量和计算特性差异极大，且发射排放随系统质量变化。本研究将ESpaS扩展为支持加速器感知建模，并评估了两个代表性系统：用于小型卫星的轻量级Jetson AGX Orin，以及由大载荷发射平台支持的高性能DGX H100。研究表明，发射排放是固定的碳开销：低质量系统可将绝对排放量降至最低，而高性能系统能更有效地分摊该成本，从而降低碳强度。因此，天地间的权衡对硬件选择高度敏感，凸显了轨道AI计算中加速器感知基线的必要性。
英文摘要
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.
