Using local models as a hybrid token-saving layer

This user runs local models for highly specific jobs while frontier models write the dispatches, watch the outputs and correct them when they drift. The approach is deliberately hybrid: local inference carries the repetitive main-task churn, while the cloud model supplies stronger orchestration and recovery. After refining the setup and keeping tasks within the local model's capability threshold, the author estimates that at least 60%+ of the time it's a net token saver on the cloud models, with that average rising as setups improve and tasks stay in scope. Hardware and model names were not specified.