GPTQ
[gptq](https://github.com/IST-DASLab/gptq) ‣ [Post-training quantization](https://texonom.com/post-training-quantization-ee4a0b4f02184b1193a68073dc60800e) 


### SOTA one-shot weight quantization method
1. Arbitrary Order insights
2. Lazy batch-updates - 메모리 처리 병목현상 개선
3. Cholesky Reformulation
Quantization 오차가 가장 적은 가중치 기준으로 정렬하고 계산수행

[GPTQ Act Order](https://texonom.com/gptq-act-order-50d18e83ed7e4e388b753f0dc6db3a97)


[GPTQ True Sequential](https://texonom.com/gptq-true-sequential-1958b85809814f3ca5c4d7d8942d0f30)

second-order information
4 bits quantization
![](https://www.notion.so/image/https%3A%2F%2Fs3-us-west-2.amazonaws.com%2Fsecure.notion-static.com%2Ffc9a9557-ca0b-4b27-bb2f-01ca097fea0c%2FUntitled.png?table=block&id=4e8f8f69-7ace-437f-8cda-454842f85589&cache=v2)



> [GPTQ: Accurate Post-Training Quantization for Generative...](https://arxiv.org/abs/2210.17323)

> [gptq](https://pypi.org/project/gptq/)









