VPTQ, or Vector Post-Training Quantization, is an innovative algorithm developed by Microsoft aimed at achieving extreme low-bit quantization for large language models (LLMs). This method allows for the compression of models, such as the 70 billion and even 405 billion parameter models, to a mere 1-2 bits without the need for retraining, while still maintaining high accuracy. The algorithm is designed to be lightweight, taking approximately 17 hours to quantize a 405 billion parameter model like Llama-3.1, and it offers agile inference capabilities with low decoding overhead, ensuring optimal throughput. The challenge of scaling model sizes has led to increased interest in low-bit quantization techniques, particularly due to the redundancy found in LLM weights. Traditional scalar-based quantization methods struggle to achieve effective low-bit representation due to numerical limitations. In contrast, VPTQ utilizes vector quantization, which compresses weight vectors into indices through lookup tables, enabling significantly lower bit-width quantization while preserving model performance. Early results from the VPTQ tech report indicate that the algorithm outperforms existing methods in terms of accuracy and throughput across various model sizes. For instance, the quantization results for LLaMA-2 models show improved performance metrics, including lower memory usage and faster token processing rates, demonstrating the effectiveness of VPTQ in practical applications. To implement VPTQ, users need to ensure they have the appropriate dependencies, including Python 3.10 or higher, and specific versions of libraries such as PyTorch and Transformers. The installation process involves setting up the CUDA environment and executing a pip command to install the VPTQ package. The repository also provides examples for generating text using pre-trained models, launching chatbots, and utilizing the Python API for model interaction. However, it is important to note that the repository serves primarily as a method for model quantization, and the performance of models provided by the open-source community cannot be guaranteed. Future plans for VPTQ include merging the quantization algorithm into public repositories, submitting the method to various inference frameworks, and enhancing the implementation of the inference kernel. The project is led by a team of contributors who acknowledge the foundational research that inspired their work. While VPTQ shows promise, it is intended for research and experimental purposes, with limitations regarding its application across different languages and tasks. The project encourages contributions and adheres to a code of conduct, ensuring a collaborative and respectful environment for developers and researchers interested in advancing the field of model quantization.