This article provides an in-depth analysis of deep learning model quantization concepts, mainstream approaches, and specific implementations in llama.cpp and vLLM inference frameworks, helping readers understand how to achieve efficient model deployment through quantization techniques.
This article provides a comprehensive overview of Llama.cpp, a high-performance, lightweight inference framework for large language models, covering its core concepts, usage methods, advanced features, and ecosystem.