Dipul's Green AI Reclamation Framework: A Practical Way to Handle Energy Limits in AI Training and Inference

This paper lays out Dipul’s Green AI Reclamation Framework, which I simply call GARF. The whole idea grew out of seeing how AI keeps hitting wall after wall when it comes to power, equipment, carbon rules, money, and speed requirements. GARF basically says that when those limits are tight, you get far more real improvement by going after the compute that is being wasted right now — idle GPU time, unnecessary calculations, models with way too many parameters, things like that. Recovering those losses beats just piling on more hardware or bigger models. I built this on basic energy balance thinking and some common sense about constraints. When I checked actual numbers from work done between 2024 and 2026 in large language model training, data center inference, and stuff running on phones or small devices, the reclamation approach often gave two to four times better results in efficiency. GARF does not invent any new science. It just gives you a clear, testable priority you can use right away in your projects, in classes, in company workflows, or even when shaping policies about sustainable tech.