The article explains that model quantization reduces storage by mapping FP16/BF16 weights to lower‑bit integers through linear quantization, illustrated by the 4.7 GB GGUF file of Llama‑3.1‑8B‑Instruct versus its 16 GB BF16 counterpart. It describes the core trick: choosing a scale factor to convert continuous weights w into quantized integers q using fewer bits, enabling efficient inference with minimal loss.