# The Cost and Network Limits of Space-Based AI Compute

arXiv: https://arxiv.org/abs/2607.14172 · HTML: https://mlsanity.com/paper/2607.14172/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 15 Jul 2026.

## Builds on (most cited first, 9 of 9)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Efficiently Scaling Transformer Inference](https://mlsanity.com/paper/2211.05102/) (2022)
- [Reducing Activation Recomputation in Large Transformer Models](https://mlsanity.com/paper/2205.05198/) (2022)
- [MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs](https://mlsanity.com/paper/2402.15627/) (2024)
- [WLB-LLM: Workload-Balanced 4D Parallelism for Large Language Model Training](https://mlsanity.com/paper/2503.17924/) (2025)
- [Performance Modeling and Workload Analysis of Distributed Large Language Model Training and Inference](https://mlsanity.com/paper/2407.14645/) (2024)
- [Towards a future space-based, highly scalable AI infrastructure system design](https://mlsanity.com/paper/2511.19468/) (2025)
- [Evaluating Cross-Architecture Performance Modeling of Distributed ML Workloads Using StableHLO](https://mlsanity.com/paper/2604.12090/) (2026)

## Abstract (arXiv)

This paper evaluates whether large-scale AI data centers deployed in low-Earth orbit (LEO) could become a cost-effective alternative to terrestrial facilities. The analysis compares orbital and ground-based systems across launch cost, power generation, cooling, radiation exposure, and atmospheric reentry, as well as compute-network performance. A key distinction is the shift from terrestrial Clos networks to space-based mesh networks using laser inter-satellite links. Using bisection bandwidth, bisection intensity, and roofline-style models, we show that while LEO-based inference may be feasible, training frontier-scale LLMs in orbit is unlikely to be competitive with terrestrial data centers.