# LRMoE: Demand-Side Load Re-Calibration for Efficient MoE Training on Memory-Constrained Nodes

> Полный текст доступен на английском

**Published:** 30 октября 2026 г.
**Author:** Jiaming Hu, Zhida Jiang, Zhaolong Xing, Yu Li, Zhen Chen, Ke Zhang
**Source:** arXiv — cs.AI
**Language:** en-US
**HTML:** https://aiqazaqstan.com/ru/articles/lrmoe-demand-side-load-re-calibration-for-efficient-moe-training-on-memo

arXiv:2610.37720v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) models offer scalable capacity for Web applications, but uneven routing creates computation and communication bottlenecks. Existing placement and replication methods redistribute this workload, yet…

> Материал агрегирован из первоисточника: **arXiv — cs.AI**
> [Читать полностью в arXiv — cs.AI](https://arxiv.org/abs/2610.37720)

**Теги:** #arXiv #GPU & Суперкомпьютеры

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