Large language models have entered the "1-bit era," where Microsoft and the University of Chinese Academy of Sciences propose the BitNet b1.58 method, which converts parameters into ternary representation, fundamentally reducing the model's memory footprint and simplifying the computational process. This method has been tested on models of various sizes, demonstrating improved speed and reduced memory usage, sparking heated discussions and debates among netizens.