A courthouse records office wants to adapt a larger pretrained model that will not fit in memory for ordinary LoRA. Which associate-depth choice lets the experiment run?
Select an answer to reveal the explanation.
Short Explanation
The larger pretrained model will not fit for ordinary LoRA. QLoRA keeps a quantized frozen base and trains small adapters so memory is no longer the blocker. It is not a new product name, a full-weight train on the same GPU, or a rack expansion.
Full Explanation
QLoRA is the associate-depth answer when memory is the binding constraint: the base stays frozen and quantized, and only small adapters train. It is a PEFT method, not a new NVIDIA product name. Full-weight training on the same GPU makes the memory problem worse, and a datacenter exam is the wrong track. Associate selection picks QLoRA so the experiment can run.