QLoRA combines quantization with LoRA to reduce VRAM requirements for finetuning large language models on consumer hardware. By storing model weights in 4-bit precision instead of 16-bit, it frees up GPU memory that would otherwise be consumed by static model states and optimizer buffers, allowing larger batch sizes and context windows without sacrificing gradient mathematical integrity.