首个为卫星上大模型推理建模碳足迹的框架，揭示能源与性能权衡。
LLMSpace: Carbon Footprint Modeling for Large Language Model Inference on LEO Satellites
- 构建联合建模框架，覆盖运行与隐含碳排放
- 发现硬件设计与寿命影响碳足迹和延迟
- 适合关注绿色太空AI的科研与工程团队
大语言模型（LLMs）带来快速攀升的能源需求，引发由大规模推理驱动的能源与碳危机。太阳能供电、具备AI能力的低地球轨道（LEO）卫星被提出以缓解地面电力消耗，但其全生命周期碳足迹因发射排放、卫星制造及抗辐射硬件要求而难以评估。本文提出首个针对AI-enabled LEO卫星上LLM推理的碳足迹建模框架——LLMSpace。该框架联合建模运行碳排放、隐含碳排放、外围子系统、抗辐射加速器与内存，以及大模型特有的预填充-解码行为和令牌生成特征。基于真实卫星与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，为可持续空间基大模型推理提供量化依据。源代码：https://github.com/UnchartedRLab/LLMSpace。
原文摘要 · Abstract (English)
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.
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