24 citations · 24 across the 4 of their papers we have counts for
4 papers
Robust LLM Training Infrastructure at ByteDance
Borui Wan, Gaohong Liu, Zuquan Song +32
The training scale of large language models (LLMs) has reached tens of thousands of GPUs and is still continuously expanding, enabling faster learning of larger models. Accompanyin…
Taming the Chaos: Coordinated Autoscaling for Heterogeneous and Disaggregated LLM Inference
Rongzhi Li, Ruogu Du, Zefang Chu +8
Serving Large Language Models (LLMs) is a GPU-intensive task where traditional autoscalers fall short, particularly for modern Prefill-Decode (P/D) disaggregated architectures. Thi…
Understanding Stragglers in Large Model Training Using What-if Analysis
Jinkun Lin, Ziheng Jiang, Zuquan Song +13
Large language model (LLM) training is one of the most demanding distributed computations today, often requiring thousands of GPUs with frequent synchronization across machines. Su…
MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs
Ziheng Jiang, Haibin Lin, Yinmin Zhong +29
We present the design, implementation and engineering experience in building and deploying MegaScale, a production system for training large language models (LLMs) at the scale of…