3 papers
cs.LG2025
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…
cs.DC2025
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…
cs.DC2025
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…