A new framework for compressing Large Language Models (LLMs) combines mixed-precision quantization and structural pruning to minimize global error propagation, achieving significant improvements in performance. This method outperforms existing techniques, reducing perplexity by up to 85% at ultra-low precisions, which could enhance LLM deployment efficiency in practical applications.