AutoGPTQ — screenshot of github.com

AutoGPTQ

AutoGPTQ is an LLM quantization package implementing the GPTQ algorithm for weight-only quantization. Note that this project is archived; consider GPTQModel for active maintenance and new model support.

Visit github.com →

Questions & Answers

What is AutoGPTQ?
AutoGPTQ is an easy-to-use Python library for weight-only quantization of Large Language Models (LLMs) based on the GPTQ algorithm. It offers user-friendly APIs for quantizing models to reduce their memory footprint and improve inference speed.
Who is AutoGPTQ designed for?
AutoGPTQ is designed for developers, researchers, and practitioners who need to deploy Large Language Models more efficiently on resource-constrained hardware or to achieve faster inference times. It is particularly useful for those working with quantized models in frameworks like Hugging Face Transformers.
What is the current maintenance status of AutoGPTQ?
AutoGPTQ is currently unmaintained and was archived by its owner on April 11, 2025, making it a read-only repository. The project's README suggests using GPTQModel for ongoing bug fixes and support for new models.
When should I consider using a quantization package like AutoGPTQ?
You should consider using a quantization package like AutoGPTQ when you need to run large language models with less VRAM, for example on consumer-grade GPUs, or to significantly boost inference speed for production deployments. It is especially relevant for edge deployments or optimizing cloud costs.
What are the installation requirements for AutoGPTQ?
AutoGPTQ is primarily available for Linux and Windows and can be installed via pip with pre-built wheels for specific CUDA (11.8, 12.1) or ROCm (5.7) versions. Source installation is also possible, requiring packages like numpy, gekko, and pandas.