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The rapid advancement of AI models is being overshadowed by significant challenges in deployment infrastructure. Companies are grappling with issues such as token bottlenecks, rising cloud costs, and suboptimal routing, all of which are impacting the performance and scalability of AI applications. To mitigate these limitations, organizations are adopting strategies like model approximation, speculative decoding, and the implementation of sophisticated routing layers. This shift underscores a growing realization that optimizing the entire AI pipeline, rather than solely focusing on model accuracy, is crucial for successful AI implementation. The initial discussion originated on Reddit’s r/artificial forum.