RunLocal AI

RunLocal AI optimizes ML model inference on edge hardware by automating the optimization, profiling, and validation processes. Users simply point the agent to their trained model and target hardware, and it iterates until the model is production-ready.

RunLocal AI utilizes a multi-agent system that processes parsed model graphs, on-device profiling, and chip vendor SDKs. This technology enables efficient debugging and optimization of ML models tailored for edge devices like Nvidia Orin and Qualcomm.

Key features of RunLocal AI include a visual graph-based orchestration system, web dashboards for experiment tracking, and an autonomous LLM-powered agent that plans and implements code changes. These features streamline the optimization process for edge AI.

RunLocal AI employs a multi-agent AI system that leverages large language models (LLMs) for planning and implementing code changes. This approach enhances the optimization process by intelligently injecting context from various artifacts.