Green AI
The practice of reducing the environmental impact of AI through energy-efficient algorithms and sustainable computing practices.
An emerging movement that prioritizes AI research and operations with low carbon footprints - optimizing model architectures for efficiency (distillation, pruning), using low-power hardware, and scheduling large training jobs when renewable energy is available. Governance initiatives include carbon accounting for AI workloads, efficiency KPIs (FLOPs per accuracy gain), and incentives for teams that meet sustainability targets without compromising quality.
A social-media company schedules its large-scale model pretraining on weekends when its data centers run exclusively on wind power, tracks the kWh consumed per training run, and publishes annual “AI carbon footprint” reports, driving research into more efficient architectures.
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What problem does Enzai solve?
Enzai provides enterprise-grade infrastructure to manage AI risk and compliance. It creates a centralized system of record where AI systems, models, datasets, and governance decisions are documented, assessed, and auditable.
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