Jetson Orin Nano Super Tries to Juggle VLA and YOLO26 on 8GB Edge Brain: Survey Says 'Cute, But Needs Cloud Therapy and Lighter Models'

A recent survey on multi-model AI resource allocation for humanoid robots highlights the challenges of running multiple AI models on edge devices like the Jetson Orin Nano Super. The device's limited memory bandwidth, power envelope, and thermal constraints make it difficult to run a full Vision-Language-Action model and several heavy vision models concurrently. To address this, researchers are exploring three major resource allocation strategies: hardware partitioning, priority-based scheduling, and offloading. Priority-based scheduling, which uses event-driven architectures and async messaging, is emerging as a promising approach. This method allows for adaptive resource allocation, graceful degradation, and real-time execution, making it suitable for production robotics systems. Companies like NVIDIA, with its Jetson Orin Nano Super, and open-source projects like OM1 and LeRobot, are developing solutions to optimize multi-model AI execution on edge devices. As the demand for edge AI continues to grow, these advancements will be crucial for enabling efficient and reliable humanoid robot operation.