Energy-Efficient Artificial Intelligence via the Universal Quantum Mesh Equation (WME System)
Abstract
Abstract Artificial Intelligence systems are rapidly expanding in scale and complexity, but their growing energy consumption presents a critical barrier for scalability and sustainability. We introduce the WME System, a proportional consensus framework derived from the universal 72–28–2 stability law. Implemented as an additional inference layer, the WME System redistributes information proportionally before expansion and anchoring, reducing redundancy without altering model capacity. Comparative tests demonstrate consistent efficiency gains across multiple domains. In a 175B-parameter language model on NVIDIA A100 hardware, energy per 1,000 tokens decreased from 0.45 kWh to 0.29 kWh (−35%), while latency remained stable (120 → 118 ms/token) and accuracy unchanged. In computer vision (ResNet-50 on ImageNet), energy was reduced by ~32%, Top-1 accuracy preserved at 76.2%, and efficiency improved from 1.8 to 2.6 images/joule. In multimodal CLIP-like models, energy consumption decreased by 30–35%, latency was stable, accuracy preserved, and long-context semantic drift reduced. These results indicate that proportional consensus can serve as a generalizable mechanism for energy-efficient AI, enabling up to 35% energy savings without measurable trade-offs in accuracy or latency. The findings highlight a scalable and architecture-agnostic approach to sustainable artificial intelligence.
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