The “multibackbone (MB) ensembling” technique for deep learning (DL) models is investigated by focusing on their application to Edge Intelligence (EI) devices with review and comparison of different multibackbone configurations. Given the constraints of EI devices, such as limited computational resources, memory, and power consumption, this study evaluates how multiple parallelly connected backbone models, specifically the EfficientNetV2 family, can enhance performance such as robustness, process time, and accuracy. On the CIFAR100 dataset, MB configurations with varying numbers of backbones (1–32) were trained and validated to analyze their impact on key metrics such as validation loss, accuracy, AUC, query per second (QPS), and latency. Results indicate that increasing the number of backbones generally improves validation accuracy and AUC while maintaining a reasonable balance between performance and computational efficiency (by QPS), making the approach feasible for deployment on EI devices.