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arXiv · 2601.02360

Heterogeneous Low-Bandwidth Pre-Training of LLMs

Yazan Obeidi·Amir Sarfi·Joel Lidin·Paul Janson·Eugene Belilovsky
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Abstract

Pre-training large language models (LLMs) increasingly requires distributed compute, yet bandwidth constraints make it difficult to scale beyond well-provisioned datacenters-especially when model parallelism forces frequent, large inter-device communications. We study whether SparseLoCo, a low-communication data parallel method based on infrequent synchronization and sparse pseudo-gradient exchange, can be combined with low-bandwidth pipeline model parallelism via activation and activation-gradient compression. We introduce a heterogeneous distributed training framework where some participants host full replicas on high-bandwidth interconnects, while resource-limited participants are grouped to jointly instantiate a replica using pipeline parallelism with subspace-projected inter-stage communication. To make the recently introduced subspace pipeline compression compatible with SparseLoCo, we study a number of adaptations. Across large-scale language modeling experiments (178M-1B parameters) on standard pretraining corpora, we find that activation compression composes with SparseLoCo at modest cost, while selective (heterogeneous) compression consistently improves the loss-communication tradeoff relative to compressing all replicas-especially at aggressive compression ratios. These results suggest a practical path to incorporating low-bandwidth model parallelism and heterogeneous participants into LLM pre-training.

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Yazan Obeidi, Amir Sarfi, Joel Lidin, Paul Janson, Eugene Belilovsky. 2026-01-05. Heterogeneous Low-Bandwidth Pre-Training of LLMs. https://arxiv.org/abs/2601.02360

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