A new GitHub project titled "Waste" aims to tackle the challenges of running enormous AI models in environments with limited RAM. By leveraging a streaming mechanism to pull activated weights directly from NVMe storage, developers can execute the hefty 2.78-trillion-parameter Kimi K3 model without the need for excessive memory allocation. This method could significantly affect engineering teams dealing with high parameter count models, particularly when physical memory constraints can stymie dep...