Recap: Where This FitsPart 1 of this series covered exact-match caching, and Part 2 covered semantic caching. Both sit in front of the model and can skip the call entirely on a hit. This article covers what happens to the requests that still reach the model: reusing computed model state, either inside your own inference engine or inside the API provider's system, plus the production details that turn a caching plan into something a team can actually run and trust.Layers 3 and 4 are two versions of the same basic idea, reusing computed model state, split by who runs the model server. If you run your own GPUs, layer 3 applies. If you call a hosted API, layer 4 applies. You rarely need both at once for the same model call.