The narrative around AI inference has been cloud-first for years. I think that’s changing and I wanted to share something concrete. Built OpenEyes - a vision system for humanoid robots that runs entirely on a Jetson Orin Nano 8GB. No cloud inference at any point. What’s running on-device: YOLO11n - object detection + distance estimation MiDaS - monocular depth MediaPipe Face - detection + landmarks MediaPipe Hands - gesture recognition MediaPipe Pose - full body pose + activity inference Why this matters for AI deployment: Cloud inference made sense when edge hardware was weak. The tradeoffs were acceptable. That calculus is shifting: Jetson Orin Nano: $249, 30-40 FPS multi-model inference, TensorRT INT8 Latency: zero network round-trip Privacy: no data leaves the device Reliability: works without internet The gap between cloud and edge capability is closing faster than most deployment architectures have adapted to. Current performance: Full stack (5 models): 10-15 FPS TensorRT INT8 optimized: 30-40 FPS Target with DLA offload: sustained 30 FPS The next interesting problem: on-device learning. Right now this is inference-only. What does continual adaptation look like without a cloud feedback loop? Full project: github.com/mandarwagh9/openeyes Where do you see the cloud vs edge inference split landing for robotics specifically? submitted by /u/Straight_Stable_6095
Originally posted by u/Straight_Stable_6095 on r/ArtificialInteligence