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Select the right hardware/VM instance types for AI/ML training

Description

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.

Solution

Evaluate and leverage the right hardware/VM instance types for training and inference of AI/ML development.

SCI Impact

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.

Assumptions

None

Considerations

None

References