The Cost and Network Limits of Space-Based AI Compute

• URL: http://arxiv.org/abs/2607.14172v1
• Date: Wed, 15 Jul 2026 09:42:59 GMT
• Title: The Cost and Network Limits of Space-Based AI Compute
• Abstract summary: Analysis compares orbital and ground-based systems across launch cost, power generation, cooling, radiation exposure, and atmospheric reentry.<n>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.
• Score: 2.436681150766912
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• FractalNet-Based Heterogeneous Federated Learning for Orbital Edge Intelligence in Satellite Mega-Constellations: A Wildfire Case Study [0.3093890460224435]
  mega-constellations are emerging as large-scale sensing, communication, and computation fabrics.<n>We propose a heterogeneous federated learning method based on the FractalNet architecture for orbital edge intelligence.<n> Experiments on simulated mega-constellations validate the approach across convergence, communication efficiency, energy adaptation, scheduled-pooling savings, robustness, and latency.
  arXiv  Detail & Related papers  (2026-09-01T08:07:39Z)
• Deep Tech to Space: Space Data Centers and AI Revolution at the Edge [5.191708026362522]
  Space Data Centers (SDCs) are software-driven, multi-tenant artificial intelligence-based service platforms capable of processing data in orbit.<n>This article presents the architecture of a Low Earth Orbit SDC satellite constellation, considering orbital design, inter-satellite links and network topology.<n>We analyze the potential technical feasibility and economic viability of SDCs using forecasting models informed by technology roadmaps.
  arXiv  Detail & Related papers  (2026-05-19T14:23:26Z)
• Space Network of Experts: Architecture and Expert Placement [53.89437186709926]
  Space data centers are envisioned as a promising platform for executing energy-intensive large language models (LLMs)<n>One key challenge, however, is the efficient distributed deployment of a large-scale LLM in a satellite network.<n>We present the Space Network of Experts (Space-XNet) framework targeting the distributed execution of a popular mixture-of-experts (MoE) model in space.
  arXiv  Detail & Related papers  (2026-05-01T08:40:31Z)
• Towards a future space-based, highly scalable AI infrastructure system design [1.6764108000740328]
  This work explores a scalable compute system for machine learning in space.<n>It uses fleets of satellites equipped with solar arrays, inter-satellite links using free-space optics, and Google tensor processing unit (TPU) accelerator chips.<n>We illustrate the basic approach to formation flight via a 81-satellite cluster of 1 km radius, and describe an approach for using high-precision ML-based models to control large-scale constellations.
  arXiv  Detail & Related papers  (2025-11-22T00:28:08Z)
• ASTREA: Introducing Agentic Intelligence for Orbital Thermal Autonomy [51.56484100374058]
  ASTREA is the first agentic system executed on flight-heritage hardware for autonomous spacecraft operations.<n>We integrate a resource-constrained Large Language Model (LLM) agent with a reinforcement learning controller in an asynchronous architecture tailored for space-qualified platforms.
  arXiv  Detail & Related papers  (2025-09-16T08:52:13Z)
• Joint AoI and Handover Optimization in Space-Air-Ground Integrated Network [48.485907216785904]
  Low Earth orbit (LEO) satellite constellations offer promising solutions with global coverage and reduced latency.<n>Yet struggle with intermittent coverage and intermittent communication windows due to orbital dynamics.<n>Our three-layer design employs hybrid free-space optical (FSO) links for high-capacity satellite-to-ground communication and reliable radio frequency (RF) links for HAP-to-ground transmission.
  arXiv  Detail & Related papers  (2025-09-16T06:16:56Z)
• FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks [18.213174641216884]
  A large number of Low Earth Orbit (LEO) satellites have been launched and deployed successfully in space by commercial companies, such as SpaceX. Due to multimodal sensors equipped by the LEO satellites, they serve not only for communication but also for various machine learning applications, such as space modulation recognition, remote sensing image classification, etc. We propose FedSN as a general FL framework to tackle the above challenges, and fully explore data diversity on LEO satellites.
  arXiv  Detail & Related papers  (2023-11-02T14:47:06Z)
• Edge Selection and Clustering for Federated Learning in Optical Inter-LEO Satellite Constellation [12.489681058742358]
  We have proposed a collaborative federated learning for low Earth orbit (FELLO) We allocate the entire process on LEOs with low payload inter-satellite transmissions, whilst the low-delay terrestrial gateway server (GS) only takes care for initial signal controlling. The proposed FELLO outperforms the conventional centralized and distributed architectures with higher classification accuracy as well as comparably lower latency of joint communication and computing.
  arXiv  Detail & Related papers  (2023-03-25T04:24:16Z)
• Learning Emergent Random Access Protocol for LEO Satellite Networks [51.575090080749554]
  We propose a novel grant-free random access solution for LEO SAT networks, dubbed emergent random access channel protocol (eRACH) eRACH is a model-free approach that emerges through interaction with the non-stationary network environment. Compared to RACH, we show from various simulations that our proposed eRACH yields 54.6% higher average network throughput.
  arXiv  Detail & Related papers  (2021-12-03T07:44:45Z)
• Deep Learning Aided Routing for Space-Air-Ground Integrated Networks Relying on Real Satellite, Flight, and Shipping Data [79.96177511319713]
  Current maritime communications mainly rely on satellites having meager transmission resources, hence suffering from poorer performance than modern terrestrial wireless networks. With the growth of transcontinental air traffic, the promising concept of aeronautical ad hoc networking relying on commercial passenger airplanes is potentially capable of enhancing satellite-based maritime communications via air-to-ground and multi-hop air-to-air links. We propose space-air-ground integrated networks (SAGINs) for supporting ubiquitous maritime communications, where the low-earth-orbit satellite constellations, passenger airplanes, terrestrial base stations, ships, respectively, serve as the space-, air-,
  arXiv  Detail & Related papers  (2021-10-28T14:12:10Z)
• Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks [51.05735925326235]
  A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency. We study the problem of forwarding packets between two faraway ground terminals, through an LEO satellite selected from an orbiting constellation. To maximize the end-to-end data rate, the satellite association and HAP location should be optimized. We tackle this problem using deep reinforcement learning (DRL) with a novel action dimension reduction technique.
  arXiv  Detail & Related papers  (2020-05-26T05:39:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.