NVIDIA Researchers Map Cross-Model KV Caches via Linear Ridge Regression to Accelerate Model Cascading Up to 25x
In multi-agent systems and tiered model cascades, routing requests across different model sizes is a standard architecture for balancing inference cost and generation quality. However, switching models mid-session has historically imposed a severe compute tax: because each neural architecture maintains its own distinct internal representations, the receiving model cannot read the key-value (KV) states generated by the previous model. The target model must recompute the entire conversation histor
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