The Race to Compress Intelligence: Quantization, Distillation, and the Future of Local AI
State-of-the-art LLMs require hundreds of gigabytes just to load — locking capable AI behind data-center walls. This article breaks down the three techniques dismantling that barrier: quantization (compressing weights from 32-bit to 4-bit and below), distillation (training smaller models to mimic larger ones), and 1-bit training (BitNet, Bonsai). Covers the math behind quantization error, the outlier and sign-loss problems, block and K-quant fixes, KV cache compression (TurboQuant, RotorQuant, IsoQuant), GGUF naming conventions, and why these complementary approaches are converging to bring frontier-class models to consumer hardware.