发表机构
1 Technical University Eindhoven, Dept. of Math. \& Computer Science P.O. Box 513, 5600 MB Eindhoven, The Netherlands Email
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究评估近地轨道部署大规模人工智能数据中心能否替代地面设施，比较轨道与地面系统多方面情况，关键在于网络类型转变，通过模型表明基于LEO推理可能可行，但训练前沿语言模型难以与地面竞争。
AI 中文摘要
本文评估部署在近地轨道（LEO）的大规模人工智能数据中心是否能成为地面设施具有成本效益的替代方案。分析比较了轨道系统和地面系统在发射成本、发电、冷却、辐射暴露、大气再入以及计算网络性能等方面的情况。关键区别在于从地面Clos网络转向使用激光星间链路的天基网状网络。通过二分带宽、二分强度和屋顶线模型表明，基于LEO的推理可能可行，但在轨道上训练前沿规模的语言模型不太可能与地面数据中心竞争。
英文摘要
This paper evaluates whether large-scale AI data centers deployed in low-Earth orbit (LEO) could become a cost-effective alternative to terrestrial facilities. The analysis compares orbital and ground-based systems across launch cost, power generation, cooling, radiation exposure, and atmospheric reentry, as well as compute-network performance. A key distinction is the shift from terrestrial Clos networks to space-based mesh networks using laser inter-satellite links. Using bisection bandwidth, bisection intensity, and roofline-style models, we show that while LEO-based inference may be feasible, training frontier-scale LLMs in orbit is unlikely to be competitive with terrestrial data centers.
