LLMEasyQuant is a user-friendly toolkit designed for the deployment of quantization on large language models (LLMs). It aims to simplify the quantization process, making it accessible to beginners and reducing the complexity of underlying structures and internal functions found in other packages like TensorRT and Quanto. The toolkit supports various quantization methods, including ZeroQuant, Symmetric 8-bit Quantization, Layer-by-Layer Quantization, SimQuant, and SmoothQuant. Each method is described in detail, with mathematical formulations and implementation examples provided. LLMEasyQuant offers a user-friendly interface, extensive customization options, and optimized performance, making it suitable for deploying LLMs on devices with limited resources. The toolkit includes features such as dynamic adjustment of quantization parameters and smoothing of activation outliers to ensure minimal loss in model accuracy.LLMEasyQuant is a user-friendly toolkit designed for the deployment of quantization on large language models (LLMs). It aims to simplify the quantization process, making it accessible to beginners and reducing the complexity of underlying structures and internal functions found in other packages like TensorRT and Quanto. The toolkit supports various quantization methods, including ZeroQuant, Symmetric 8-bit Quantization, Layer-by-Layer Quantization, SimQuant, and SmoothQuant. Each method is described in detail, with mathematical formulations and implementation examples provided. LLMEasyQuant offers a user-friendly interface, extensive customization options, and optimized performance, making it suitable for deploying LLMs on devices with limited resources. The toolkit includes features such as dynamic adjustment of quantization parameters and smoothing of activation outliers to ensure minimal loss in model accuracy.