Hot AI in Cold Space: Thermal-Crosstalk-Aware Scheduling for Sustainable Orbital AI Clusters

2026-06-23

AI Summary

This study addresses thermal accumulation in high-density orbital AI clusters caused by convective and radiative thermal crosstalk, which leads to performance throttling and reduced hardware lifespan. The work proposes a novel paradigm—thermal-aware heterogeneity—that treats spatial cooling disparities as a primary resource dimension, thereby transcending the limitations of conventional uniform load distribution. By developing a thermal-aware scheduling framework (TLB) grounded in real-time fluid temperature and radiation absorption metrics, the approach dynamically migrates large language model workloads to nodes with optimal cooling conditions, achieving thermal load balancing. This strategy effectively mitigates thermal bottlenecks, restores model floating-point utilization (MFU), reduces thermal stress, enhances computational efficiency, and extends the operational lifetime of orbital hardware, thus enabling sustainable AI deployment in space environments.

Abstract

Terrestrial AI training faces an unsustainable energy and water crisis, positioning Orbital Data Centers (ODCs) as a "zero operational carbon" alternative. However, the sub-$10μ\text{s}$ communication latency required for distributed Large Language Model (LLM) training forces ODCs into extreme physical density, triggering a critical "Proximity-Thermal Paradox." As these high-density systems scale into Monolithic Structures or Proximity Swarms, they suffer from intense thermal-fluid crosstalk (heat traps in shared cooling loops) and thermal-radiative crosstalk (mutual heating that blocks deep-space cooling radiators). If left unmitigated, this persistent heat stagnation not only triggers severe thermal throttling that degrades training throughput, but also induces severe thermal fatigue, drastically shortening hardware lifespans and generating premature space e-waste. To make orbital AI truly sustainable, this position paper challenges traditional uniform load-sharing. We propose the Thermal-Aware Heterogeneity Thesis, which treats spatial cooling variances as a primary resource management dimension. Building on this, we introduce Thermal-Load Balancing (TLB), a software framework that dynamically migrates LLM workloads to the coolest available units based on instantaneous fluid temperatures or absorbed radiation. Our analysis demonstrates that TLB resolves thermal bottlenecks to restore Model Flops Utilization (MFU), while simultaneously reducing physical thermal stress. Extending the operational lifespan of orbital hardware is crucial to amortize the massive embodied carbon of rocket launches, outlining a necessary pathway to scale orbital AI without accelerating e-waste.

Problem

• thermal crosstalk
• orbital AI clusters
• thermal throttling
• space e-waste
• sustainable computing

Innovation

• Thermal-Load Balancing
• Orbital Data Centers
• thermal crosstalk
• sustainable AI
• LLM training