新闻
Sonar-MoE: In-Backward Expert Updates for Memory-Efficient Mixture-of-Experts Training
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Sonar-MoE reduces GPU memory for training mixture-of-experts models by updating expert gradients during backward pass instead of storing them.
- 为何重要
- Matters for engineers training large MoE models who face GPU memory constraints and need faster training with maintained model quality.
- 注意
- Results shown on two specific models; unclear how broadly this generalizes across different MoE architectures, scales, and hardware configurations.
- fine-tun
- mixture-of-experts
- olmo
- granite
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