HexiScale introduces a novel system that allows efficient LLM training across diverse GPU types, moving beyond the traditional homogeneous data center approach.

• Employs asymmetric partitioning of computations across data, pipeline, and tensor parallelism dimensions
• Optimizes training efficiency through heterogeneity-aware tensor parallelism
• Achieves improved resource utilization in mixed-GPU environments
• Demonstrates practical scaling of large language models on diverse hardware setups

This research enables organizations to leverage existing heterogeneous infrastructure for LLM training, potentially reducing costs and democratizing access to advanced AI development capabilities.

HexiScale: Accommodating Large Language Model Training over Heterogeneous Environment