## AI Inference: A New Approach to Reduce Consumption Energy efficiency has become a priority in the development and deployment of artificial intelligence models. Long-term inference can exceed the environmental impact of training. A new study proposes a bio-inspired framework that maps protein-folding energy basins to inference costs, controlling execution via a decaying threshold. ## Optimization with NVIDIA Triton and FastAPI The system admits a request only when the ratio between expected utility and energy consumption is favorable, prioritizing efficiency. The technical team evaluated DistilBERT and ResNet-18 served via FastAPI with ONNX Runtime and NVIDIA Triton on an RTX 4000 Ada GPU. Tests revealed that the bio-controller reduces processing times by 42% compared to standard execution, with minimal accuracy degradation (less than 0.5%). ## The benefits of closed-loop The study also defined the efficiency boundaries between lightweight local serving (ORT) and managed batching (Triton). The results connect biophysical energy models to Green MLOps and offer a practical, auditable basis for energy-aware inference in production. This closed-loop approach represents a step towards more sustainable artificial intelligence systems.