Quantization and Pruning Methods to Make Your LLM Leaner

Quantization and pruning are key techniques to reduce large language model sizes without significant performance loss, enabling more efficient deployment. Quantization reduces number precision, while pruning removes unnecessary parameters, both saving memory and compute resources.