Orbital Ingress: Thermal Dynamics and Latency Optimization for Distributed AI Infrastructure in Low Earth Orbit

Terrestrial data centers are approaching a fundamental thermodynamic ceiling. The global AI infrastructure buildout of the 2020s has produced a class of facilities consuming hundreds of megawatts per campus, with Power Usage Effectiveness (PUE) values averaging 1.58 across the industry—meaning that for every watt spent on computation, an additional 0.58 watts is spent on cooling alone. As AI model inference scales from billions to trillions of parameters, this heat-death constraint will impose either a hard ceiling on deployable compute density or an economically catastrophic escalation in energy and water consumption. This paper proposes a radical but technically grounded alternative: the orbital sharding of a Global AI Knowledge Base across the Starlink Low Earth Orbit (LEO) constellation, leveraging the thermodynamic properties of deep space—specifically, the passive radiative sink of the vacuum environment—to eliminate active cooling overhead entirely. Building directly on the parallelized Retrieval-Augmented Generation (RAG) architecture detailed in our prior work (Scalable RAG Architecture for High-Volume Unstructured Archives, April 2026), we extend the distributed ingestion and sharded vector storage model from terrestrial server clusters to orbital satellite nodes. The core thesis is as follows: a Starlink satellite in shadow arc experiences passive radiative cooling to temperatures as low as −180 °C without any active thermal management system. At these temperatures, silicon compute hardware—field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), and compressed vector index storage—operates at dramatically reduced leakage current, enabling higher sustained compute density per watt than any terrestrial facility. Coupled with the latency advantage of LEO communication paths (approximately 20–40 ms round-trip at 550 km altitude versus 60–80 ms for mid-latitude terrestrial routing), an orbital knowledge base offers lower query latency, higher thermal headroom, zero water consumption, and a physically air-gapped security posture unachievable on the ground. We present a complete system architecture: a four-layer Orbital Ingress pipeline (ground ingestion, thermal-gated uplink, orbital vector nodes, and latency-optimized query routing), an Orbital Shard Map partitioning a 200-million-vector Global Knowledge Base across five Starlink orbital rings, and a thermal dynamics model quantifying the cooling advantage across the orbital day-night cycle. Performance projections indicate a PUE of approximately 1.05 for orbital nodes (versus 1.58 terrestrial), sustained GPU-equivalent inference throughput at 2–3× the burst capacity of thermally throttled terrestrial equivalents, and ground-to-ground query latency under 500 ms for retrieval across the full orbital index.