LoRA is a parameter-efficient fine-tuning method that freezes the original model weights and injects small trainable matrices into each layer, dramatically reducing the memory and compute needed. Instead of updating billions of parameters, you train only a few million, making custom model adaptation affordable on consumer GPUs. A Moroccan startup can fine-tune an open-source LLM on Darija customer data with a single GPU instead of an expensive cluster.