Green AI Carbon Optimizer: Carbon-Efficient Training Location Recommendation and Global AI Energy Demand Forecasting
arXiv:2606.14707v1 Announce Type: cross Abstract: AI training and deployment consume substantial electricity, but carbon outcomes remain weakly integrated into routine model development decisions. This paper presents Green AI Carbon Optimizer with two primary contributions: (i) a carbon aware cloud region recommendation method for training workloads, and (ii) a power law forecasting pipeline for global AI energy demand. For location recommendation, we combine regional grid carbon intensity, renewable share, and data center Power Usage Effectiveness (PUE) into a unified scoring model across 100
The accelerating compute intensity for AI training and deployment is making the previously unaddressed carbon footprint of AI an increasingly urgent concern.
This development introduces tangible tools to integrate carbon efficiency into AI development, transforming how infrastructure decisions are made in a rapidly growing, energy-hungry sector.
AI development decisions will increasingly incorporate carbon intensity and PUE alongside traditional cost and performance metrics, driving demand for greener compute infrastructure.
- · Cloud providers with green energy grids
- · Renewable energy producers
- · Data center efficiency technology providers
- · Cloud providers reliant on high-carbon grids
- · AI developers ignoring carbon footprint
- · Regions with high carbon intensity electricity
Widespread adoption of carbon-aware AI training will lead to a significant reduction in the carbon footprint of AI workloads.
Increased investment in renewable energy infrastructure and energy-efficient data center technologies will be driven by market demand for 'green AI'.
Geopolitical competition for AI compute will expand to include carbon efficiency as a strategic advantage, influencing innovation and national policy in energy production and infrastructure.
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Read at arXiv cs.AI