Distributed Training Frameworks for Large Language Models: Architectures, Challenges, and Innovations

Abstract

The exponential growth of large language models has necessitated the development of sophisticated distributed training frameworks to efficiently manage computational resources, model complexity, and parallelization strategies. This article presents a comprehensive analysis of distributed training architectures for large language models, examining their technical foundations, implementation challenges, and recent innovations. Beginning with a detailed exploration of core parallelization strategies—data parallelism, model parallelism, and pipeline parallelism—the article evaluates how each approach addresses fundamental constraints in training massive neural networks. It then examines leading frameworks, including Megatron-LM, DeepSpeed, and Alpa, highlighting their unique approaches to memory optimization, parallelization automation, and computational efficiency. The article further investigates persistent challenges in distributed training, including communication overhead, memory management limitations, and fault tolerance requirements. Finally, it explores emerging trends in heterogeneous computing and energy efficiency that promise to shape the future development of distributed training systems. Throughout, the article emphasizes how these frameworks and techniques collectively enable the continued scaling of language models while managing the associated computational demands.