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Currently, the world experiences an unprecedentedly increasing generation of application data, from sensor measurements to video streams, thanks to the extreme connectivity capability provided by 5G networks. Going beyond 5G technology, such data aim to be ingested by Artificial Intelligence (AI) functions instantiated in the network to facilitate informed decisions, essential for the operation of applications, such as automated driving and factory automation. Nonetheless, while computing platforms hosting Machine Learning (ML) models are ever powerful, their energy footprint is a key impeding factor towards realizing a wireless network as a sustainable intelligent platform. Focusing on a beyond 5G wireless network, overlaid by a Multi-access Edge Computing (MEC) infrastructure with inferencing capabilities, our paper tackles the problem of energy-aware dependable inference by considering inference effectiveness as value of a goal that needs to be accomplished by paying the minimum price in energy consumption. Both MEC-assisted standalone and ensemble inference options are evaluated. It is shown that, for some system scenarios, goal effectiveness above 84% is achieved and sustained even by relaxing communication reliability requirements by one decimal digit, while enjoying a device radio energy consumption reduction of almost 23% at the same time. Also, ensemble inference is shown to improve system-wide energy efficiency and even achieve higher goal effectiveness, as compared to the standalone case for some system parameterizations.