Squeezing AI Brains: A Guide to Model Quantization

Quantization techniques make large AI models accessible by reducing their size without significantly sacrificing performance. By trading numerical precision for efficiency, these methods enable AI to run on consumer devices. The process converts high-precision formats (FP32/FP16) to compact representations, with solutions like GGUF for CPU optimization and GPTQ for GPUs. Modern approaches like LLM.int8() and NF4 address the "outlier problem" where extreme values compromise accuracy, enabling mainstream AI adoption on everyday devices.