Phase transitions in large language model compression

This perspective argues that Large Language Models exhibit Model Phase Transitions: performance collapses beyond critical compression thresholds. We analyze structural, numerical, and algebraic redundancy across pruning, quantization, and low-rank decomposition techniques. These sources are orthogonal, enabling a criticality-aware compression framework that achieves near-lossless compression to 10% of the original size. This shift proves that compressing a giant is more effective than training a dwarf for efficient, sustainable AI deployment.