This article was revised with later test data. Earlier figures and tables on this page were replaced. Each Ollama runner keeps a prefix cache that can skip re-evaluating a shared system prompt. Nine of seventeen local builds reused the cached system prompt on nearly every new conversation. Four others re-evaluated a 1,024-token batch of it each time. Four more re-evaluated all of it once per system prompt, up to 37 seconds on the largest. The model's memory type and runner set the group, not the prompt. In freemansoft/Flutter-AdaptiveCards a demonstration Dart chat server asks a local Ollama model for an answer as Adaptive Card JSON. That is a strict, closed-vocabulary schema, which a Flutter app renders as interactive UI. The card system prompt that describes the element vocabulary runs to about 3,800 to 4,400 tokens, by the model's tokenizer. The chat server replays up to ten prior exchanges on every turn. Each Ollama runner keeps a prefix cache, so a request whose open...