A research-backed, open-source Green Orchestration Framework for sustainable AI inference. Built as an ISM Final Product to show that AI efficiency and environmental responsibility can go hand in hand.
Training and running large language models consumes enormous amounts of electricity. A single ChatGPT query uses roughly 10x the energy of a Google search, and the gap keeps growing as models get larger and usage scales globally.
The core inefficiency is that every query, whether "hi" or "design a distributed system," gets routed to the same massive model. There is no intelligence in the routing, no awareness of energy cost, and no attempt at optimization.
GreenInfer tackles this at the infrastructure level, before a single GPU cycle is burned on inference.
Research shows that 60 to 70 percent of real-world AI queries are simple enough to be handled by small, efficient models, if the routing decision is made correctly. Papers like FrugalGPT and the cascading LLM literature back this up with empirical results.
Combining smart routing with prompt optimization and carbon-aware scheduling creates a compounding effect where savings from each layer stack together.
The models, tools, and APIs exist today. GreenInfer integrates them into a unified, developer-friendly framework.
Frisco ISD student building GreenInfer as an Independent Study and Mentorship final project. The project brings together a year of research in AI efficiency, energy systems, and sustainable computing. Srinesh previously built GreenPromptsOptimizer, a T5-based prompt compression model that now forms the first layer of the GreenInfer pipeline. GreenInfer represents a full-stack research effort spanning model training, framework engineering, backend deployment, and public product launch, built with the goal of making green AI genuinely accessible to developers everywhere.
Marta Adamska is a PhD Candidate at the University of Lancaster whose research sits at the intersection of AI systems, sustainability, and computational efficiency. Ms. Adamska's expertise in sustainable computing has been deeply instrumental to this project, providing the research direction, key papers, and guidance that shaped GreenInfer from an early idea into a working framework. I am grateful for her continued mentorship and for pushing the technical depth of this work throughout the research process.
Papers recommended by Ms. Adamska and reviewed during research. These directly shaped the design decisions in GreenInfer.
GreenInfer is built to be shared. The framework is on GitHub for any developer to use, extend, or build on top of.