The motivation for parameter-efficient fine-tuning lies in the ability to improve model performance without drastically increasing computational requirements. While it may not directly reduce runtime or GPU memory usage, it allows for better utilization of existing resources. By fine-tuning only a subset of the model parameters, we can achieve similar performance gains as full fine-tuning while minimizing the computational overhead. This approach is particularly useful when working with limited computing resources or when fine-tuning large models that would otherwise be impractical to train from scratch.