Spatial computing is evolving towards leveraging data streaming for computationally demanding applications, facilitating a shift to lightweight, untethered, and standalone devices. These devices are therefore ideal candidates for co-processing, where real-time context understanding and low-latency data streaming are fundamental for seamless, general-purpose Mixed Reality (MR) experiences. This paper demonstrates and evaluates a scalable approach to augmented contextual understanding in MR by implementing multi-modal edge AI co-processing through a Hailo-8 AI accelerator, a low-power ARM-based single board computer (SBC), and the Magic Leap 2 AR headset. The proposed system utilises the native WebRTC streaming capabilities of the Magic Leap 2 to continuously stream camera data to the edge co-processor, where a collection of vision AI models-object detection, pose estimation, face recognition, and depth estimation-are executed. The resulting inferences are then streamed back to the headset for spatial re-projection and transmitted to cloud-based systems for further integration with large-scale AI models, such as LLMs and VLMs. This seamless integration enhances real-time contextual understanding in MR while facilitating advanced multi-modal, multi-device collaboration, supporting richer, scalable spatial cognition across distributed systems.