QLoRA beats full fine-tuning as default for style tuning, 7B test finds

The comparison was motivated by a common early decision when fine-tuning LLMs on proprietary data: whether QLoRA is sufficient or full fine-tuning is necessary. The author standardized the model, dataset, and training conditions to isolate the cost differences between the two approaches.

Beyond raw memory use, the author breaks cost into four components: GPU memory, training time, storage, and operational burden. QLoRA reduces the number of parameters that carry gradients and optimizer states, making it easier to start on smaller GPUs, but the 4-bit quantized computation can slow each step. Full fine-tuning updates all parameters, so checkpoints are the entire model, increasing storage and management load as experiments repeat.

The recommended workflow is to first build a baseline with QLoRA and check improvement on evaluation data, then move to full fine-tuning only if limits appear. This order is presented as a practical development cycle, with the caveat that updating all parameters on small datasets can lead to overfitting or degradation of the model's original capabilities.