Large language models (LLMs) impose rapidly growing energy demands, creating an emerging energy and carbon crisis driven by large-scale inference. Solar-powered, AI-enabled low Earth orbit (LEO) satellites have been proposed to mitigate terrestrial electricity consumption, but their lifecycle carbon footprint remains poorly understood due to launch emissions, satellite manufacturing, and radiation-hardened hardware requirements. This paper presents \textit{LLMSpace}, the first carbon modeling framework for LLM inference on AI-enabled LEO satellites. LLMSpace jointly models operational and embodied carbon, peripheral subsystems, radiation-hardened accelerators and memories, and LLM-specific workload characteristics such as prefill-decode behavior and token generation. Using realistic satellite and GPU configurations, LLMSpace reveals key trade-offs among carbon footprint, inference latency, hardware design, and operational lifetime for sustainable space-based LLM inference. Source code: https://github.com/UnchartedRLab/LLMSpace.

翻译：大语言模型（LLM）带来了快速增长的能源需求，引发了由大规模推理驱动的新兴能源与碳危机。太阳能供电、支持人工智能的低地球轨道卫星已被提出用于缓解地面电力消耗，但由于发射排放、卫星制造以及抗辐射硬件要求，其全生命周期碳足迹仍知之甚少。本文提出LLMSpace——首个面向人工智能低地球轨道卫星上大语言模型推理的碳建模框架。LLMSpace联合建模了运行碳与隐含碳、外围子系统、抗辐射加速器与存储器，以及大语言模型特定工作负载特征（如预填充-解码行为和令牌生成）。通过使用真实的卫星与GPU配置，LLMSpace揭示了碳足迹、推理延迟、硬件设计与运行寿命之间的关键权衡，为可持续发展的天基大语言模型推理提供支撑。源代码：https://github.com/UnchartedRLab/LLMSpace。