This blog post proposes an optimization architecture aimed at improving AI computational efficiency before inference, focusing on minimizing unnecessary data movement and computation. The suggested architecture includes a dedicated optimization layer that filters, deduplicates, and preprocesses data, ultimately reducing the workload on large AI models. This approach not only targets token economy but seeks to enhance overall infrastructure scale, energy efficiency, and model throughput as AI interactions increase.