9 citations · 10 across the 5 of their papers we have counts for
5 papers
Mechanism-Aligned Observability for Training Instability of Large Language Models
Ruixuan Huang, Yipei Wang, Hantao Huang +7
Frontier large language model training consumes massive accelerator fleets over long wall-clock times, making stability failures extremely costly. However, global symptoms such as…
On the Spectral Flattening of Quantized Embeddings
Junlin Huang, Wenyi Fang, Zhenheng Tang +5
Training Large Language Models (LLMs) at ultra-low precision is critically impeded by instability rooted in the conflict between discrete quantization constraints and the intrinsic…
ElasWave: An Elastic-Native System for Scalable Hybrid-Parallel Training
Xueze Kang, Guangyu Xiang, Yuxin Wang +16
Large-scale LLM pretraining now runs across -- accelerators, making failures routine and elasticity mandatory. We posit that an elastic-native training system must join…
BurstGPT: A Real-world Workload Dataset to Optimize LLM Serving Systems
Yuxin Wang, Yuhan Chen, Zeyu Li +11
Serving systems for Large Language Models (LLMs) are often optimized to improve quality of service (QoS) and throughput. However, due to the lack of open-source LLM serving workloa…
Fault-Tolerant Hybrid-Parallel Training at Scale with Reliable and Efficient In-memory Checkpointing
Yuxin Wang, Xueze Kang, Shaohuai Shi +8
To efficiently scale large model (LM) training, researchers transition from data parallelism (DP) to hybrid parallelism (HP) on GPU clusters, which frequently experience hardware a…