[2607.14172] The Cost and Network Limits of Space-Based AI Compute

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Computer Science > Distributed, Parallel, and Cluster Computing

arXiv:2607.14172 (cs)

[Submitted on 15 Jul 2026]

Title:The Cost and Network Limits of Space-Based AI Compute

Authors:Kees van Berkel<br>View a PDF of the paper titled The Cost and Network Limits of Space-Based AI Compute, by Kees van Berkel

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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:<br>arXiv:2607.14172 [cs.DC]

(or<br>arXiv:2607.14172v1 [cs.DC] for this version)

https://doi.org/10.48550/arXiv.2607.14172

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Kees van Berkel [view email]<br>[v1]<br>Wed, 15 Jul 2026 09:42:59 UTC (2,583 KB)

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