Open Source AI May Reduce Energy Demands
By: Sayeed Choudhury, Hoda Heidari, Tori Qiu, and Keith Webster
It will be important to tap open source models to reduce AI’s energy demand, and Carnegie Mellon University is at the forefront of exploring this opportunity.
Why it matters: Transparency about the AI model development cycle, from design to deployment, underscore opportunities for optimization of energy consumption, which could lead to greater efficiency and less energy usage. Openness in AI is a framework for such transparency, with open source AI being a foundation.
Catch up quick: There is growing evidence that AI design and implementation choices related to a model’s architecture, hardware, cloud infrastructure, data processing, and algorithms have profound impact on energy usage, particularly regarding training from scratch or fine tuning an existing model. Models that are released without adequate information about these facets of their design, development, and use make it difficult, if not impossible, for third parties to assess the energy consumption, carbon footprint, water use, or other impacts of the training and running of these models and their downstream applications.
The opportunity ahead: Open source software, as defined by the Open Source Definition, represents a well established, successful underpinning for transparency in software development. While sometimes cast in an altruist light, open source software has resulted in nearly $9 trillion of value, while reducing production costs by a factor of 3.5. A similarly principled framework for AI, including transparency around model weights, training data, code, and governance, also may enable both accountability and energy-conscious innovation.
What we’re doing: The Carnegie Mellon University-led Open Forum for AI (OFAI) is developing an Openness in AI framework, which includes the Open Source AI Definition (OSAID) as one of its underpinnings.
- The OSAID is an initial community-driven definition that reflects the technical and legal dimensions for data, code, and weights of an AI system.
- The Openness in AI framework expands upon the view of open source to open governance, with multiple stakeholders working together toward transparent, responsible, participatory AI.
- OFAI’s comprehensive program, including research, aims to examine the benefits and risks of openness across various dimensions of AI development.
What we found: One of OFAI’s initial research outputs is a study of the implications from regulators’ choices of open source AI, and responses from entities who create general purpose AI models, and specialists who fine-tune those models for specialized tasks or domains. This research provides an initial framework for optimizing policy choices regarding open source AI, while considering the impact on AI model developers and contributors. Extensions of this work could examine how such policy choices can encourage energy-friendly innovations (i.e., Meta’s open-weight models inspired more efficient fine-tuning methods such as QLoRA) and choices that affect energy usage or consumption.
Policy takeaways: Policymakers and research funders can play a pivotal role by incentivizing transparency and openness in AI development as part of broader climate and energy strategies. By linking openness to grants, procurement standards, or regulatory frameworks, governments can help drive innovation toward more energy-efficient and accountable AI systems.
The bottom line: Addressing the growing energy demands for AI development will require a comprehensive approach spanning AI computational demands, AI immediate applications, and AI systemic impacts. For the computational aspects, transparency can play a critical role in nudging AI companies toward a more participatory approach. Such an approach would include academia, local utilities, municipalities, state and federal governments, and public citizens/ratepayers, for a coherent energy and electrification strategy and policy.