arXiv:2608. 14557v1 Announce Type: cross Abstract: Low Earth Orbit (LEO) computing is emerging for low-latency, globally distributed AI services, enabled by advances in satellite constellations and reusable launch systems.
By Nisha Sarwar, Lei Jiang, Fan Chen
arXiv:2607. 14172v1 Announce Type: cross Abstract: This paper evaluates whether large-scale AI data centers deployed in low-Earth orbit (LEO) could become a cost-effective alternative to terrestrial facilities.
By Kees van Berkel
arXiv:2608.24516v1 Announce Type: cross
Abstract: Satellite-based distributed learning promises to train machine-learning models directly in orbit using massive, globally dispersed sensor data, there...
By Hao Wu, Kin Whye Chew, Yizhan Han, Han Li, Jingxian Wang
arXiv:2605.19892v2 Announce Type: replace-cross
Abstract: Dramatic cost reductions driven by private sector innovations have led to a rapid increase in the number of satellites in orbit and a corresp...
By Jonas Weiss, Patricia Sagmeister, Gabriel Maiolini Capez, Dinesh Verma, Roberto Garello, Alberto Perotti, Dawid Lazaj, Alicja Musial, Jakub Nalepa, Thomas Morf, Martin Schmatz, Marek Krawczyk, Mateusz Przeliorz, Kevin Roche, Sagar Tayal, Mahalakshmi Lakshminarayanan, Nicolas Long\'ep\'e, Pierre-Philippe Mathieu, Agata Wijata
Satellite-based distributed learning promises to train machine-learning models directly in orbit using massive, globally dispersed sensor data, thereby avoiding large-scale data downloads to ground se...
ShardMeter is a lightweight analytical performance model that predicts end-to-end runtime for transformer-based workloads across sharded, distributed, and decentralized training setups. By taking a model’s characteristics and a target hardware topology as input, it estimates per-GPU and per-island throughput, training cost, total wall-clock time, and pinpoints performance bottlenecks. The model reveals diminishing-return regimes with increasing island size, quantifies compute- versus communication-bound scaling, evaluates hyperparameter trade-offs, and models cost-throughput for large-scale decentralized training, enabling rapid exploration of configuration space and near-optimal deployment plans.
By Tim Beringer (Technical University of Darmstadt), Patrick Diem (Technical University of Darmstadt), Felix Wolf (Technical University of Darmstadt), Arya Mazaheri (Technical University of Darmstadt, PanocularAI)