Fine-tuning refines a pretrained language model by further training it on targeted data, allowing it to specialize in specific tasks. This process depends not only on algorithms but also on the hardware that supports them, especially memory subsystems that manage large volumes of data.

This engineering report explores the effect of fine-tuning large language models with high-bandwidth memory, showing how system architecture and memory influence model performance. It offers observations for how hardware constraints shape AI workflows and outcomes. This perspective encourages a deeper understanding of AI systems as tightly integrated with the infrastructure they run on, where hardware and software evolve together to support learning and adaptation.