Foundation models are increasingly deployed at the edge to deliver low-latency, privacy-preserving AI services. However, edge environments impose strict constraints on compute, memory, energy and network resources, making model deployment, inference acceleration and system evaluation challenging. This Recommendation defines the differentiated needs for edge domain inference systems, providing standard for the evaluation of functionality and performance metrics for foundational models’ inference engines, to address the challenges of model deployment in edge domain applications.