Quantization is essential for reducing the size and computational requirements of AI models, especially for deployment on edge devices with limited resources. Large neural networks require high memory ...

I want to use pytorch-quantization to perform classification quantization of Deepstream7, which can be done normally in deepstream6. The process is to use torch-tensorrt==1.4.0 and ...

In today’s deep learning landscape, optimizing models for deployment in resource-constrained environments is more important than ever. Weight quantization addresses this need by reducing the precision ...

The general definition of quantization states that it is the process of mapping continuous infinite values to a smaller set of discrete finite values. In this blog, we will talk about quantization in ...

Model quantization bridges the gap between the computational limitations of edge devices and the demands for highly accurate models and real-time intelligent applications. The convergence of ...

Abstract: This study systematically investigates how quantization, a key technique for the efficient deployment of large language models (LLMs), affects model safety. We specifically focus on ...