AI Infrastructure in Space: How Far Can We Go?

2026-08-21

AI Summary

论文探讨了卫星作为AI计算平台的问题，通过构建系统层管理太空与地面资源，并基于实际案例研究提出了面向太空的资源管理和持续AI服务方案。

Abstract

Satellites are becoming programmable computing platforms capable of running increasingly demanding AI workloads. This shift raises a systems problem: how can AI services remain deployable, manageable, and recoverable after launch when compute capacity, connectivity, energy, and thermal headroom vary over orbital time? This paper develops a systems vision for AI infrastructure in space. We define it as the systems layer that manages AI capabilities across spacecraft, orbital networks, ground stations, and cloud backends, while treating orbital and physical state as part of the resource model. We synthesize relevant foundations from terrestrial AI infrastructure, satellite networking, and satellite edge computing, and examine the physical constraints that directly shape system design. We further ground this vision in three in-orbit case studies spanning the node, platform, and service levels. Telemetry from BUPT-1 satellite shows that usable compute capacity is bounded by thermal and energy envelopes. SateLight on BUPT-2 satellite reduces application-update transmission latency by 56.54% on average and up to 91.18%, with 100% update correctness. A stateful VLM serving case further shows that thermal interruptions make execution-state recovery a first-class systems problem. These observations motivate a research agenda for space-native resource management, lifecycle support, and sustained AI service across space and ground.

Problem

• AI services
• satellites
• computing platforms
• orbital time
• resource management

Innovation

• AI Infrastructure in Space
• Orbital and Physical State as Resources
• Space-Native Resource Management