At scale, a recommendation system can end up repeating the same work over and over. The same popular items get scored for similar users, the same ads are ranked for overlapping audiences, and identical queries trigger identical predictions.

A large chunk of these computations are redundant.

Model caching avoids this by storing predictions and reusing them when the same inputs show up again. No changes to the model, just smarter reuse. The result is lower latency and reduced compute cost, especially for GPU-heavy workloads.