A research-backed, open-source Green Orchestration Framework for sustainable AI inference - built as an ISM Final Product, proving that AI doesn't have to cost the planet.
Training and running large language models consumes enormous amounts of electricity. A single ChatGPT query uses roughly 10× the energy of a Google search - and the gap is growing as models get larger and usage explodes.
The core inefficiency: every query - from "hi" to "write me a PhD thesis" - gets routed to the same massive model. There's no intelligence in the routing. No awareness of energy cost. No optimization.
GreenInfer addresses this at the infrastructure level, before a single GPU cycle is burned.
Research shows that 60–70% of real-world AI queries are simple enough to be handled by small, efficient models - if the routing decision is made correctly.
Combining smart routing with prompt optimization (reducing wasted tokens before inference) and carbon-aware scheduling creates a compounding effect: savings from every component stack.
This isn't a future solution. The models, tools, and APIs exist today. GreenInfer integrates them into a unified framework anyone can use.
Frisco ISD student building GreenInfer as an Independent Study & Mentorship final project. The project synthesizes 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. This project represents a full-stack effort: research, model training, framework design, and deployment - built with the goal of making green AI accessible to every developer.
Marta Adamska is a PhD Candidate at the University of Lancaster whose research intersects AI systems, sustainability, and computational efficiency. As the project mentor, Dr. Adamska helped refine the scope of GreenInfer - prioritizing the orchestration framework as the core deliverable and the chatbot as its showcase. Her guidance emphasized that the project's feasibility is grounded in the foundational work of the prompt optimization model, and helped shape the research direction toward a rigorous, achievable timeline.
GreenInfer is built to be shared. The framework code will be published on GitHub for any developer to integrate into their own AI applications.