对比卫星算力系统碳排放，发现硬件选型决定环保性
Orbital AI Computing: Carbon Tradeoffs Across Satellite Scale
- 用加速器感知模型分析卫星算力碳足迹
- 小卫星减重可降绝对排放，大算力可摊薄发射成本
- 为轨道AI提供基于硬件的碳排放评估基准
低地球轨道（LEO）计算正兴起，用于低延迟、全球分布的AI服务，得益于卫星星座和可重复使用发射系统的进步。然而其可持续性仍不明确。现有工作提出ESpaS框架估算全生命周期碳强度，但采用通用数据中心配置建模，未涵盖现代AI硬件——其功耗、重量和算力特性差异大，且发射排放随系统质量增加。本文扩展ESpaS，引入加速器感知建模，评估两类代表性系统：小型卫星搭载的Jetson AGX Orin，以及大型运载平台支持的DGX H100。结果表明，发射环节构成固定碳开销：轻量级系统降低绝对排放，高性能系统则更有效分摊该成本，从而降低碳强度。因此，空间-地面权衡高度依赖硬件选择，凸显在轨道AI计算中建立加速器感知基线的必要性。
原文摘要 · Abstract (English)
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.
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