In recent years, the compression of large language models (LLMs) has emerged as a key problem in facilitating LLM deployment on resource-limited devices, reducing compute costs, and mitigating the environmental footprint due to large-scale AI infrastructure. Here, we establish the foundations of LLM quantization from a rate-distortion theory perspective and propose a quantization technique based on simple rate-distortion optimization. Our technique scales to models containing hundreds of billions of weight parameters and offers users the flexibility to compress models, post-training, to a model size or accuracy specified by the user.
@article{arxiv.2505.03031,
title = {Radio: Rate-Distortion Optimization for Large Language Model Compression},
author = {Sean I. Young},
journal= {arXiv preprint arXiv:2505.03031},
year = {2025}
}