BitNet b1.58 operates with a radical constraint: every weight is restricted to just three values (-1, 0, or 1), making it fundamentally different from conventional floating-point models. Despite this extreme quantization baked in from training rather than applied afterward, it maintains coherent language generation at 2 billion parameters.