Using DistributedDataParallel to train a base model from scratch in the cloud (www.gilesthomas.com)

In the latest update on developing a large language model (LLM) from scratch, the author successfully transitioned from single-GPU training on an RTX 3090 to a multi-GPU setup using DistributedDataParallel (DDP) on Lambda Labs. This experimentation, which cost $215.16 and drastically reduced training time from 48 hours to under four hours using an 8x A100 GPU configuration, highlights the efficiency gains achievable with multi-GPU training. The author aims to not only improve training efficiency but also explore factors affecting model quality by comparing results across different machine sizes. This transition is significant for the AI/ML community as it emphasizes the importance of distributed training, providing insights into the technical nuances of porting code from single-GPU to multi-GPU architectures. By leveraging DDP over the traditional DataParallel approach, the author reduces communication overhead and enhances GPU utilization, which is crucial for scaling LLM training. The experience serves as a valuable resource for researchers and developers looking to optimize their training workflows and refine their models, with practical lessons documented for future reference.