Segmentation is fundamental to edge vision—from drivable surface detection to industrial inspection. But how do different approaches actually perform on resource-constrained hardware? This session benchmarks segmentation models across NVIDIA Jetson, NXP i.MX, Kinara NPU and Raspberry Pi with Hailo acceleration, comparing semantic and instance segmentation using YOLO family models with deployment-optimized architectures. We examine both on-target accuracy and real-world inference latency, with recorded demonstrations from actual devices. The talk culminates with SAM-class foundation models on edge hardware—not real time, but illustrating the trade-off between zero-shot generalization and purpose-trained models achieving interactive frame rates. Attendees will leave with practical guidance for matching segmentation model complexity to hardware capability.