BitDP: Ultra-low-bit Communication for Data Parallelism in LLM Training

Abstract

Training large language models (LLMs) with billions of parameters on trillion-token datasets requires distributed data parallelism at increasingly large scales, where gradient synchronization becomes a communication bottleneck, especially in bandwidth-constrained environments. Although gradient quantization presents a promising solution, it faces two key challenges: maintaining training stability and accuracy for transformer architectures and adapting to modern distributed communication systems. In this paper, we propose BitDP, an ultra-low-bit gradient quantization system that reduces communication costs by up to 32× while preserving model accuracy with less than 1% performance degradation. Our approach achieves numerical stability for large transformer models and seamlessly integrates with existing infrastructures. We evaluate BitDP