ScaleAcross Explorer: Exploring Communication Optimization for Scale-Across AI Model Training
The rapid scaling of large language model training requires distributing GPU resources across multiple data center buildings and regions. We refer to such paradigm as "scale-across" training. As infrastructure expands, the system design space becomes increasingly intricate, encompassing new model architectures, hardware heterogeneity, and evolving communication patterns. Drawing from Meta's production experience, we highlight the complexities of deploying training jobs across a few data centers housing hundreds of thousands of GPUs. To accelerate exploration of the large design space and to enable efficient training for frontier model development, we conduct in-depth characterization of t...
| Authors | Minghao Li, Alicia Golden, Samuel Hsia et al. |
|---|---|
| Primary category | cs.DC |
| Published | 23 May 2026 |
| Last updated at source | 23 May 2026 |
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