通过集成太阳能、计算与散热板实现的低质量轨道 AI 推理
摘要
我们描述并分析了一种用于 SSO 计算卫星的分布式计算架构，可能实现每发射吨（包括部署和维持位置）可获得超过 100 kW 的计算功率。该架构将太阳能电池板、散热器和计算功能共位置于多个小型面板构成的大型阵列中。 resulting large vapor chamber radiator area per panel should permit ICs to operate at junction temperatures near 40*C with benefits in compute efficiency and reliability. Using the structure of the radiator to support the solar cells may also yield a specific power of about 500 W/kg compared to less than 100 for existing conventional implementations. Assuming development of custom solutions for all components, a 16 MW computation, 150 ton satellite comprising a 20 m x 2200 m grid of 16,000 panels can fit in a single Starship hold. The concept is scalable to much larger satellites with higher mass payloads or using on-orbit assembly. We consider panel sizes from 1 to 4 m2 to allow trading vapor chamber heat transport with compute efficiency and inter-panel communication. Assuming a 1 kW/panel design, 512-panel subarrays of the satellite can run a representative inference-only LLM with 500,000 token context window and 128 attention blocks, at a rate of 553 tokens/sec/session, across 256 simultaneous in-flight sessions. A full satellite could support 31 such subarrays, for >7900 inferences at a time.
引用
@article{arxiv.2604.07760,
title = {Reduced-Mass Orbital AI Inference via Integrated Solar, Compute, and Radiator Panels},
author = {Stephen Gaalema and Samuel Indyk and Clinton Staley},
journal= {arXiv preprint arXiv:2604.07760},
year = {2026}
}
备注
13 pages, 8 tables, 9 figures