Streaming DiLoCo: Efficient Distributed Training of Large Language Models

The research focuses on improving distributed training of Large Language Models (LLMs) by introducing Streaming DiLoCo, a method that reduces communication costs without compromising model quality. The paper presents innovations like streaming synchronization, overlapping communication, and gradient quantization to achieve this efficiency and scalability.

Streaming DiLoCo introduces three main improvements: streaming synchronization reduces peak bandwidth, overlapping communication with computation hides latency, and quantization compresses data exchanged between workers. The research shows similar performance to Data-Parallel training but with significantly reduced bandwidth, making it a promising approach for distributed LLM training.

Read full paper: https://arxiv.org/abs/2501.18512v1

Tags: Distributed Training, Large Language Models, Machine Learning, Communication Efficiency, Gradient Compression