QERA introduces an analytical framework that enables extremely low-precision quantization while maintaining model performance through error reconstruction.

• Combines quantization with low-rank approximation to significantly reduce model size and computation costs
• Provides a mathematical foundation for quantizing weights to extremely low precision (even 1-bit)
• Offsets quantization errors with high-precision error reconstruction terms
• Enables practical deployment of large language models on resource-constrained devices

This research addresses a critical engineering challenge in AI deployment: how to run increasingly large models efficiently without sacrificing performance, potentially revolutionizing edge computing applications for LLMs.

QERA: an Analytical Framework for Quantization Error Reconstruction