Apple's recent announcement highlights the development of on-device AI models that utilize small, fine-tuned LoRA (Low-Rank Adaptation) adapters to perform multiple tasks while maintaining privacy and efficiency. This approach, which involves dynamically hot-swapping these adapters on a single small language model (SLM), allows for high performance akin to larger models like GPT-4 but at a reduced size and cost. Predibase, a company with experience in this domain, has developed an open-source framework called LoRAX that enables the deployment of numerous task-specific adapters on a single base model. This architecture is seen as a transformative strategy for deploying AI systems, allowing for scalable and specialized applications without the need for extensive resources. Predibase offers tools and resources for organizations to adopt this model, reflecting a shift towards using many specialized AI assistants in complex workflows.