A small startup with a limited budget and access to a single GPU wants to adapt a very large, pre-trained language model (over 100 billion parameters) to perform a specialized task: classifying customer support tickets. Given their significant hardware and financial constraints, which of the following approaches represents the most justifiable and resource-conscious strategy?

Analyzing Fine-Tuning Trade-Offs

Rationale for Parameter-Efficient Training

When adapting a large pre-trained model to a new task, employing a parameter-efficient training method will always result in a model that performs identically to one trained via full fine-tuning, with the sole advantage being reduced computational requirements.