Agentic AI systems face a significant bottleneck when switching between different-sized Large Language Models, as the receiving model must recompute the entire conversation history. This recomputation incurs substantial compute costs and latency, hindering long-horizon, multi-LLM workflows. Researchers at Nvidia have developed a cross-model KV cache transfer technique to address this issue. This method directly maps the pre-computed Key-Value cache from a source model to a target model. This process significantly reduces compute costs and latency for enterprise AI applications. Experiments demonstrate that this linear mapping technique is 2.7 to 25 times faster than recomputing the cache. It also preserves up to 98% of the target model's standalone accuracy on compatible model pairs. The technique leverages the linear structure of the KV cache, employing simple algebraic methods rather than expensive deep learning training. This enables seamless upgrades from smaller to larger models for enhanced output quality. It also facilitates cost reduction by transitioning from larger to smaller models after initial heavy processing. While initially focused on within-family model transfers, the framework shows potential for broader applications. This advancement offers a crucial tool for scaling multi-model agentic systems while managing inference costs.