Managing Multiple Local LLMs on Constrained Hardware with GGUF Switchboard

Developer Pradeep Gudipati has introduced GGUF Switchboard, an open-source tool designed to manage multiple local Large Language Models (LLMs) on hardware with limited VRAM, such as an NVIDIA RTX 3060. Recognizing the challenge of running various specialized models for coding, reasoning, and AI agents simultaneously on a 12GB GPU, the author developed a scheduler that acts as an OpenAI-compatible API gateway. The system dynamically handles the lifecycle of models—draining, unloading, and loading them based on incoming requests—to optimize GPU utilization. By abstracting the inference infrastructure, GGUF Switchboard allows developers to treat multiple local models as shared resources without needing to manage individual backends like llama.cpp or vLLM manually. The project aims to make operating a collection of local models more efficient and accessible for users with constrained hardware, encouraging community feedback to refine support for diverse model and hardware configurations.