Challenges and Solutions for Scaling up Large Language Models in Distributed Computing Environments

Abstract

Large language models have demonstrated remarkable capabilities in natural language processing tasks, yet scaling them efficiently in distributed computing environments presents significant challenges. This paper explores key obstacles such as computational resource allocation, data parallelism, and communication overheads inherent in scaling up models like GPT-3 and its successors. Solutions include optimizing model architecture for distributed training, improving communication protocols, and leveraging advanced hardware accelerators. By addressing these challenges, this research aims to enhance the scalability and efficiency of large language models, paving the way for their broader deployment in diverse applications.