# QuIP: 2-Bit Quantization of Large Language Models With Guarantees

**Authors:** Jerry Chee, Yaohui Cai, Volodymyr Kuleshov, Christopher De Sa  
**arXiv:** [2307.13304](https://arxiv.org/abs/2307.13304)  
**Date:** 2023-07-25  
**Summary page:** https://www.summarizepaper.com/en/paper/2307.13304v1/

## Key points

- This work focuses on post-training parameter quantization in large language models (LLMs)
- Introduces a new method called Quantization with Incoherence Processing (QuIP)
- QuIP consists of two steps: adaptive rounding procedure and efficient pre- and post-processing
- Quantization benefits from incoherent weight and Hessian matrices
- Provides the first theoretical analysis for an LLM-scale quantization algorithm
- Incoherence preprocessing improves existing quantization algorithms
- Achieves remarkable results using only two bits per weight
- Code implementation for QuIP available on GitHub at https://github.com/jerry-chee/QuIP
- Advances post-training parameter quantization techniques for large language models
- Demonstrates effectiveness of QuIP in achieving high-quality results with minimal memory usage.

## Summary

This work focuses on post-training parameter quantization in large language models (LLMs) and introduces a new method called Quantization with Incoherence Processing (QuIP). QuIP consists of two steps: (1) an adaptive rounding procedure that minimizes a quadratic proxy objective, and (2) efficient pre- and post-processing that ensures weight and Hessian incoherence through multiplication by random orthogonal matrices. The authors observe that quantization benefits from incoherent weight and Hessian matrices, meaning that accurate rounding of weights is more effective when the weights and the directions in which they are important are unaligned with the coordinate axes. In addition to introducing QuIP, this study provides the first theoretical analysis for an LLM-scale quantization algorithm. The authors demonstrate that their theory also applies to an existing method called OPTQ. Empirically, they find that their incoherence preprocessing improves several existing quantization algorithms. Notably, they achieve remarkable results by using only two bits per weight, making QuIP the first LLM quantization method to produce viable outcomes at such low bit precision. The authors provide their code implementation for QuIP on GitHub at https://github.com/jerry-chee/QuIP. Overall, this research contributes to advancing post-training parameter quantization techniques for large language models and demonstrates the effectiveness of QuIP in achieving high-quality results with minimal memory usage.