Researchers from Stanford University and UC Berkeley have developed S-LoRA, a technique that significantly reduces the cost of deploying fine-tuned large language models (LLMs). S-LoRA uses dynamic memory management and a “Unified Paging” mechanism to serve multiple models on a single GPU, enabling businesses to run hundreds or even thousands of models without incurring prohibitive costs. This advancement could unlock numerous new applications for LLMs in areas such as content creation and customer service.
