@inproceedings{shin-etal-2026-geomesh,
title = "{G}eo{M}esh: Workload-Balanced and Sign-Compressed Geo-Distributed {LLM} Training",
author = "Shin, Changyong and
Park, Jaerim and
Kang, Minchul and
Go, Younghun and
Niu, Zhixiong and
Xiong, Yongqiang and
Yang, Gyeongsik and
Yoo, Chuck",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.720/",
pages = "15131--15152",
ISBN = "979-8-89176-462-0",
abstract = "Large language models are increasingly trained on GPUs distributed across multiple regions, but geo-distributed training is challenging in practice. Real clusters often contain GPUs with different speeds and memory capacities, and they communicate over slow wide-area networks. Our analysis shows that this creates serious problems: existing synchronous methods preserve stable updates, but fast GPUs wait up to 20.9{\%} of their runtime for slower ones, and all workers spend, on average, 65.8{\%} of their runtime on synchronization. Recent asynchronous methods reduce waiting time but worsen the model accuracy due to stale updates. To address the problems, we present GeoMesh, a synchronous geo-distributed training framework for heterogeneous GPUs. GeoMesh balances per-worker workloads by assigning each GPU a suitable batch size and number of inner steps, so faster GPUs do more useful work instead of waiting. It also reduces communication volume by nearly 32$\times$ by exchanging compressed sign-based pseudo-gradients with lightweight magnitude and token count. Across heterogeneous GPUs and Azure-derived WAN, GeoMesh reduces time-to-target perplexity by up to 70.2{\%} over representative baselines and lowers straggler- and WAN-induced GPU idle by up to 8.0$\times$ and 5.6$\times$, respectively, while preserving comparable zero-shot accuracy."
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<abstract>Large language models are increasingly trained on GPUs distributed across multiple regions, but geo-distributed training is challenging in practice. Real clusters often contain GPUs with different speeds and memory capacities, and they communicate over slow wide-area networks. Our analysis shows that this creates serious problems: existing synchronous methods preserve stable updates, but fast GPUs wait up to 20.9% of their runtime for slower ones, and all workers spend, on average, 65.8% of their runtime on synchronization. Recent asynchronous methods reduce waiting time but worsen the model accuracy due to stale updates. To address the problems, we present GeoMesh, a synchronous geo-distributed training framework for heterogeneous GPUs. GeoMesh balances per-worker workloads by assigning each GPU a suitable batch size and number of inner steps, so faster GPUs do more useful work instead of waiting. It also reduces communication volume by nearly 32\times by exchanging compressed sign-based pseudo-gradients with lightweight magnitude and token count. Across heterogeneous GPUs and Azure-derived WAN, GeoMesh reduces time-to-target perplexity by up to 70.2% over representative baselines and lowers straggler- and WAN-induced GPU idle by up to 8.0\times and 5.6\times, respectively, while preserving comparable zero-shot accuracy.</abstract>
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%0 Conference Proceedings
%T GeoMesh: Workload-Balanced and Sign-Compressed Geo-Distributed LLM Training
%A Shin, Changyong
%A Park, Jaerim
%A Kang, Minchul
%A Go, Younghun
%A Niu, Zhixiong
%A Xiong, Yongqiang
%A Yang, Gyeongsik
%A Yoo, Chuck
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F shin-etal-2026-geomesh
%X Large language models are increasingly trained on GPUs distributed across multiple regions, but geo-distributed training is challenging in practice. Real clusters often contain GPUs with different speeds and memory capacities, and they communicate over slow wide-area networks. Our analysis shows that this creates serious problems: existing synchronous methods preserve stable updates, but fast GPUs wait up to 20.9% of their runtime for slower ones, and all workers spend, on average, 65.8% of their runtime on synchronization. Recent asynchronous methods reduce waiting time but worsen the model accuracy due to stale updates. To address the problems, we present GeoMesh, a synchronous geo-distributed training framework for heterogeneous GPUs. GeoMesh balances per-worker workloads by assigning each GPU a suitable batch size and number of inner steps, so faster GPUs do more useful work instead of waiting. It also reduces communication volume by nearly 32\times by exchanging compressed sign-based pseudo-gradients with lightweight magnitude and token count. Across heterogeneous GPUs and Azure-derived WAN, GeoMesh reduces time-to-target perplexity by up to 70.2% over representative baselines and lowers straggler- and WAN-induced GPU idle by up to 8.0\times and 5.6\times, respectively, while preserving comparable zero-shot accuracy.
%U https://aclanthology.org/2026.findings-emnlp.720/
%P 15131-15152
Markdown (Informal)
[GeoMesh: Workload-Balanced and Sign-Compressed Geo-Distributed LLM Training](https://aclanthology.org/2026.findings-emnlp.720/) (Shin et al., Findings 2026)
ACL
- Changyong Shin, Jaerim Park, Minchul Kang, Younghun Go, Zhixiong Niu, Yongqiang Xiong, Gyeongsik Yang, and Chuck Yoo. 2026. GeoMesh: Workload-Balanced and Sign-Compressed Geo-Distributed LLM Training. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 15131–15152, Budapest, Hungary. Association for Computational Linguistics.