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Independent testing confirms GLAI’s impact on energy efficiency and sustainability.
Artificial Intelligence (AI) is transforming industries, but not without cost. The energy required to train and retrain models can be immense, contributing to increased operational expenses and a growing carbon footprint. This reality is pushing companies to seek new approaches that combine performance with sustainability.
At Qsimov, our response to this challenge is GreenLightning AI (GLAI), a new approach designed to significantly reduce the computational burden of AI model updates, while maintaining precision and reliability.
But how does GLAI perform in real-world conditions?
To objectively assess its environmental and operational impact, BE-IN-G, through its Platform and specifically the Be-SCIM (Software Carbon Intensity Manager) module, independently designed, executed, and analyzed a series of tests using GLAI. These evaluations were conducted entirely by their team, ensuring external validation through a rigorous and impartial lens.
The results from BE-IN-G provide concrete evidence of GLAI’s effectiveness in enabling more sustainable AI practices:
Faster training and retraining: GLAI enables significantly accelerated retraining processes without compromising model accuracy, reducing the time required to update models with new data. GLAI demonstrated an impressive performance boost, achieving execution speeds 315x times faster than traditional retraining methods. This drastic reduction in processing time translates into greater efficiency, lower computational costs, and a more sustainable AI workflow.
Enhanced energy efficiency: The application of GLAI led to substantial reductions in energy consumption. The test results reveal a 239x reduction in energy consumption, showcasing GLAI’s exceptional efficiency. This significant improvement translates into substantial cost savings, making AI retraining not only more sustainable but also highly cost-effective.
Carbon emissions reduction: GLAI reduces carbon emissions by a factor of 264x, making AI retraining significantly greener and more sustainable without compromising performance.
Stable and reliable execution: Across multiple iterations, GLAI demonstrated high stability and low variance in all key performance indicators, ensuring consistent and predictable improvements.
Minimal deviation in model precision: Unlike traditional retraining approaches, which may lead to accuracy trade-offs, GLAI maintained the precision level required throughout the process, reinforcing its reliability for real-world applications.
The independent testing performed by Being Tech was key in quantifying GLAI’s efficiency gains. Using the Be-SCIM module, they measured software carbon intensity and other metrics with precision, providing transparency and credibility to the results.
For Qsimov, this collaboration represents more than just numbers, it reinforces the importance of external benchmarking when developing responsible AI technologies. This test validates what we are building: a system that increases efficiency, and aligns with emerging sustainability frameworks.
As AI becomes more central to every sector, the pressure to balance innovation with sustainability grows. GLAI offers organizations a powerful tool to do just that, enabling:
We believe that responsible innovation must be measurable. If you’re ready to evolve your AI infrastructure with sustainability at the core, GLAI is a proven path forward.
Interested in learning more? Follow us for updates on energy-efficient AI, or get in touch to explore how GLAI can transform your retraining processes.
