AI agents are hitting a costly memory wall.
When an agent switches between models, the new model often recomputes the entire conversation.
Nvidia researchers introduced cross-model KV cache transfer using simple linear math.
On compatible model pairs, it ran 2.7 to 25 times faster than recomputing, while retaining up to 98% of standalone accuracy.
That is the direction AI infrastructure needs: faster context handling, lower GPU waste, and practical systems that scale.
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Originally posted on facebook