Nvidia cuts AI handoff costs with simple linear math

Nvidia researchers have developed a cross-model KV cache transfer technique that eliminates costly recomputation when agentic AI systems hand tasks between models. Instead of forcing the receiving model to reprocess entire conversations from scratch, the method maps cached data directly from one model to another using simple linear math. The technique runs 2.7 to 25 times faster than traditional recomputation while retaining up to 98% accuracy. Unlike previous approaches, it requires no expensive deep learning model, making it a practical solution for enterprises running long-horizon, multi-LLM workflows.