E-Sparse stands out from other pruning methods in terms of efficiency due to its unique approach. Traditional pruning techniques often require retraining the pruned network to recover accuracy, which can be computationally expensive and time-consuming. In contrast, E-Sparse introduces an entropy-based metric that evaluates parameter importance without modifying the remaining weights. This one-shot pruning method allows for significant speedup in model inference without the need for weight updates or retraining. By incorporating information richness and amplitude metrics into the evaluation process, E-Sparse can efficiently prune large language models (LLMs) while maintaining acceptable accuracy levels.