LLMSpace: Carbon Footprint Modeling for LLM Inference on LEO Satellites

Large language model (LLM) inference imposes growing energy and carbon costs. Solar-powered low Earth orbit (LEO) platforms can reduce reliance on grid electricity, but manufacturing, launch, peripheral, and radiation-hardening requirements introduce substantial embodied carbon. We present LLMSpace, a component-level framework for modeling the carbon and performance of LEO-based LLM inference, including compute hardware, solar arrays, batteries, radiative cooling, launch emissions, and radiation-hardening overheads. Across the evaluated scenarios, launch carbon intensity is the primary determinant of the orbital–terrestrial carbon crossover, while radiation-hardening power overhead is an important secondary factor. Workload and GPU evaluations further show that prompt and output lengths, batch size, request rate, and hardware throughput substantially affect inference energy and amortized embodied carbon per token. These results show that LEO inference is not inherently lower-carbon; its carbon benefit depends on launch accounting, mission lifetime, terrestrial grid intensity, hardware efficiency, workload characteristics, and system utilization.