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cs.LG2025
Efficient Long-context Language Model Training by Core Attention Disaggregation
Yonghao Zhuang, Junda Chen, Bo Pang +6
We present core attention disaggregation (CAD), a technique that improves long-context large language model training by decoupling the core attention computation, softmax(QK^T)V, f…
cs.LG2024
Toward Inference-optimal Mixture-of-Expert Large Language Models
Longfei Yun, Yonghao Zhuang, Yao Fu +2
Mixture-of-Expert (MoE) based large language models (LLMs), such as the recent Mixtral and DeepSeek-MoE, have shown great promise in scaling model size without suffering from the q…
cs.LG2024
DISTFLASHATTN: Distributed Memory-efficient Attention for Long-context LLMs Training
Dacheng Li, Rulin Shao, Anze Xie +5
FlashAttention (Dao, 2023) effectively reduces the quadratic peak memory usage to linear in training transformer-based large language models (LLMs) on a single GPU. In this paper,…