12 citations · 29 across the 17 of their papers we have counts for
Showing 2023Show all
3 papers · 1 filter
cs.PF2023
Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models
Longteng Zhang, Xiang Liu, Zeyu Li +8
Large Language Models (LLMs) have seen great advance in both academia and industry, and their popularity results in numerous open-source frameworks and techniques in accelerating L…
cs.DC2023★ 1 cited
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…
cs.DC2023★ 10 cited
FusionAI: Decentralized Training and Deploying LLMs with Massive Consumer-Level GPUs
Zhenheng Tang, Yuxin Wang, Xin He +8
The rapid growth of memory and computation requirements of large language models (LLMs) has outpaced the development of hardware, hindering people who lack large-scale high-end GPU…