New Nvidia paper shows, one LLM can reuse another model's prompt memory instead of processing the whole prompt again.

A simple linear converter lets related LLMs reuse cached prompt memory and skip reprocessing long conversations.

Normally, when a system switches models, the new model rereads the whole conversation to build its key-value cache, a stored record used during generation.

Their mapper learns how each target attention head relates to several useful source layers, then removes position information before conversion and restores it afterward.

It learns from 500 calibration sequences, requires no backpropagation, and was tested only on related models with matching cache shapes.

Across 6 model pairs from Qwen3, Llama 3.1, and Ministral 3, 4 retained 73% to 98% of the target model's normal benchmark accuracy.

Across tested transfers, conversion ran 2.7 to 25 times faster, while Qwen3 handoffs stayed stable across 10 turns.

The failures revealed that success depends less on total conversion error than on whether errors land where attention actually reads.

• arxiv. org/abs/2608.03893

Title: "Cross-Model KV Cache Transfer in LLM Families: A Closed-Form Linear Mapping for Prefill Reuse"