The Cost and Network Limits of Space-Based AI Compute
A research paper evaluates whether deploying large AI data centers in low-Earth orbit (LEO) could be cost-effective. It compares orbital and terrestrial systems across factors like launch cost, power, cooling, and network performance. The study finds that while LEO inference may be feasible, training frontier-scale AI models in space is unlikely to compete with ground-based facilities.
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[Submitted on 15 Jul 2026]
Title:The Cost and Network Limits of Space-Based AI Compute
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Abstract: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.
Subjects:
Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.14172 [cs.DC]
(or arXiv:2607.14172v1 [cs.DC] for this version)
https://doi.org/10.48550/arXiv.2607.14172
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Kees van Berkel [view email] [v1] Wed, 15 Jul 2026 09:42:59 UTC (2,583 KB)
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