RECOMMENDATION FRAMEWORK OF MODEL AND HARDWARE FOR ENERGY-EFFICIENT MACHINE LEARNING WORKLOADS

One example method includes obtaining input including characteristics of a machine learning (ML) model, specifications of a hardware configuration on which the ML model has been run, and characteristics of a prospective workload, estimating, given the hardware configuration, an energy efficiency of the ML model, using a similarity measure to find peer ML models of the ML model, and each peer ML model is more energy efficient, given the hardware configuration, than the ML model, ranking the peer ML models as suggested alternatives to the ML model, storing, in a database, the characteristics of the ML model, the energy efficiency of the ML model, and the specifications of the hardware configuration, and transmitting the specifications of the hardware configuration, along with workload characteristics, to a recipient.