Select the right hardware/VM instance types for AI/ML training
Training an AI model has a significant carbon footprint. Selecting the right hardware/VM instance types for training is one of the choices you should make as part of your energy-efficient AI/ML process. For instance, custom application-specific integrated circuits (ASICs) and field-programmable gate arrays (FPGAs) are provided or supported by cloud vendors which provide better energy efficiency and inference for AI models than conventional chips.
Evaluate and leverage the right hardware/VM instance types for training and inference of AI/ML development.
SCI = (E * I) + M per R
Software Carbon Intensity Spec
Selecting the right hardware/VM types impacts SCI as follows:
E: The right hardware/VM type provides better energy efficiency and inference for AI models, reducing the energy consumption of your AI/ML processes overall.
M: By reducing the total number of servers required to run a process, the total embodied carbon is lower.