The rapidly growing edge computing platforms, coupled with the still imperfect edge infrastructure, present an excellent opportunity for the emergence of new edge hardware. However, it remains unclear whether alternative architectures built from energy-efficient mobile System-on-Chips (SoCs) can meet the stringent performance, cost, and energy demands of modern edge workloads. In this paper, we propose a new type of edge server composed of 60 Qualcomm Snapdragon 865 mobile SoCs in a 2U rack, referred to as SoC Cluster. We demonstrate its successful deployment on existing edge cloud platforms and its ability to natively serve mobile cloud gaming services. Despite the emergence of new hardware on edge platforms and its successful operation in serving mobile cloud gaming, our trace analysis revealed low hardware utilization and significant dynamic fluctuations in usage. To assess its broader applicability, we conducted the first measurement study of SoC Cluster to reveal its ability to run two popular and modern edge applications: deep learning inference and video transcoding. We developed a cross-platform benchmark suite to evaluate throughput, latency, power consumption, and application-specific metrics like video quality. We then directly compare SoC Cluster with a traditional edge server equipped with Intel CPUs and NVIDIA GPUs in terms of energy efficiency, space efficiency, and monetary cost. Results show that SoC Cluster exhibits up to 6.5? higher energy efficiency and 7.7? higher space efficiency. We also disclose its limitations in serving computation-intensive workloads such as large deep learning models. The outcomes provide insightful implications and offer practical direction for refining SoC Cluster toward broader deployment in edge scenarios.