arXiv:2511.11505v3 Announce Type: replace Abstract: Blocking communication presents a major hurdle in running MoEs efficiently in distributed settings. To address this, we present FarSkip-Collective which modifies the architecture of modern models to enable overlapping of their computation with communication. Our approach modifies the architecture to skip connections in the model and it is unclear a priori whether the modified model architecture can remain as capable, especially for large state-of-the-art models and while modifying all of the model layers. We answer this question in the affirm
The increasing scale and complexity of Mixture of Experts (MoE) models necessitate more efficient distributed training methods, making advancements in communication protocols critical for continued progress.
Efficient distributed training directly impacts the scalability and cost-effectiveness of large AI models, determining who can develop and deploy cutting-edge AI.
The ability to overlap computation with communication in MoE models could significantly reduce training times and resource requirements, accelerating AI development.
- · Large AI model developers
- · Cloud computing providers
- · AI hardware manufacturers
- · AI research institutions
- · Competitors with less efficient distributed training approaches
- · AI operations teams reliant on traditional blocking communication
Faster and cheaper training of large AI models becomes possible.
This could democratize access to training cutting-edge AI models, or conversely, further solidify the lead of those with the most compute.
The reduced barriers to training could lead to an accelerated pace of AI innovation and new applications across various sectors.
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