1-BIT LLMS COULD SOLVE AI'S ENERGY DEMANDS

Summary from the TLDR Web Dev newsletter

LLMs demand a lot of energy, but researchers are finding ways to reduce their size by quantization - representing model parameters with only 1 or -1. The two main approaches are post-training quantization (PTQ) and quantization-aware training (QAT). PTQ is currently more popular. Despite lower perplexity scores, 1-bit LLMs are much more energy efficient and faster on customized chips.