# LLMSpace: Carbon Footprint Modeling for Large Language Model Inference on LEO Satellites

arXiv: https://arxiv.org/abs/2605.05615 · HTML: https://mlsanity.com/paper/2605.05615/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 53 references that are in the map.
- First posted on arXiv 7 May 2026.

## Builds on (most cited first, 10 of 10)

- [How Hungry is AI? Benchmarking Energy, Water, and Carbon Footprint of LLM Inference](https://mlsanity.com/paper/2505.09598/) (2025)
- [LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models](https://mlsanity.com/paper/2309.14393/) (2023)
- [LLMCO2: Advancing Accurate Carbon Footprint Prediction for LLM Inferences](https://mlsanity.com/paper/2410.02950/) (2024)
- [Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations](https://mlsanity.com/paper/2507.11417/) (2025)
- [A Systematic Characterization of LLM Inference on GPUs](https://mlsanity.com/paper/2512.01644/) (2025)
- [The Dirty Secret of SSDs: Embodied Carbon](https://mlsanity.com/paper/2207.10793/) (2022)
- [Trends in AI Supercomputers](https://mlsanity.com/paper/2504.16026/) (2025)
- [Empirically-Calibrated H100 Node Power Models for Reducing Uncertainty in AI Training Energy Estimation](https://mlsanity.com/paper/2506.14551/) (2025)
- [Dirty Bits in Low-Earth Orbit: The Carbon Footprint of Launching Computers](https://mlsanity.com/paper/2508.06250/) (2025)
- [CO2-Meter: A Comprehensive Carbon Footprint Estimator for LLMs on Edge Devices](https://mlsanity.com/paper/2511.08575/) (2025)

## Abstract (arXiv)

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 textitLLMSpace, 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.