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Power demand surges from a single AI prompt put post-transformer architectures in focus

Surging electricity demand from the spread of AI is exposing the limits of transformer-based large language models and boosting calls for next-generation architectures. With many models still relying on more compute, layers and data, analysts expect data centre spending to rise sharply by 2030. Inference-focused models can consume far more power per prompt. Industry attention is turning to post-transformer designs that activate only relevant parts of a model, aiming to cut inference costs, reduce token use and remain compatible with existing infrastructure.