As discussed in the previous chapter, basic Post-Training Quantization (PTQ) methods, such as MinMax scaling, offer a computationally inexpensive way to quantize models. However, when targeting aggressive quantization levels like 4-bit (INT4) or even 3-bit integers, these simpler approaches often result in a noticeable drop in model accuracy. The reason is that they typically determine quantization parameters (scale and zero-point) based solely on the range or distribution of weights or activations within a layer, without directly considering the impact of the quantization error on the model's subsequent computations and final output.

GPTQ, which stands for Generalized Post-Training Quantization, was developed to address this limitation. It represents a more sophisticated PTQ approach specifically designed to maintain higher accuracy, particularly at very low bit-widths. The central idea behind GPTQ is to perform weight quantization in a way that minimizes the error introduced into the layer's output, rather than just minimizing the error between the original and quantized weights themselves.

Instead of quantizing all weights in a layer independently based on simple statistics, GPTQ operates layer by layer. For each layer, it iteratively selects weights to quantize and determines their quantized values ( $W_q$ ) such that the squared error between the original layer output ( $WX$ ) and the quantized layer output ( $W_qX$ ) is minimized, using a small batch of calibration data ( $X$ ). Mathematically, the objective is to find $W_q$ that minimizes:

||W X - W_q X||_2^2

To achieve this minimization efficiently, GPTQ employs an approximate second-order method. It uses information derived from the Hessian matrix (the matrix of second partial derivatives) of the layer's reconstruction error with respect to the weights. This allows GPTQ to make more informed decisions about how to round each weight value during quantization, considering the sensitivity of the layer's output to changes in that specific weight. This process is more computationally intensive than basic calibration but significantly less demanding than Quantization-Aware Training (QAT), as it only requires processing the calibration data once per layer.

The primary benefit of using GPTQ is its ability to achieve substantially better accuracy preservation compared to simpler PTQ methods, especially for INT4 and INT3 quantization. It often allows models to be quantized to these low bit-widths with minimal degradation in performance on downstream tasks, making it a popular choice for deploying large language models where memory and computational resources are constrained. GPTQ remains a post-training technique, meaning it doesn't require access to the original training dataset or involve any model retraining or fine-tuning, only a small amount of representative calibration data.

In the following section, we will examine the mechanics of the GPTQ algorithm in more detail, exploring how it uses the Hessian information and performs the layer-wise quantization process.