KV Cache (Key-Value Cache) stores intermediate computation results during AI model inference, avoiding repeated processing of previous inputs. It helps the model 'remember' past tokens, dramatically speeding up predictions without re-calculating everything from scratch.
During sequential input processing, the model generates key and value pairs for each token. KV Cache saves these pairs and reuses them in subsequent steps, so the model only processes new tokens instead of the entire input. This reduces computation and inference latency significantly.
For AI product managers, KV Cache means faster response times and lower compute costs—crucial for real-time applications. It enables scalable, efficient deployment of large language models, improving user experience and reducing infrastructure expenses while maintaining high-quality outputs.