How to Transfer KV Cache Between LLMs Without Re-Prefill (2.7-25x Faster)
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TL;DR When you swap between different-sized LLMs in production (14B to 32B, for example), the receiving model has to re-run the entire prefill from scratch. This paper proposes a closed-form linear mapper that transfers the KV cache across models in the same family, achieving 2.7-25x speedup over re-prefill with 73-98% accuracy retention on most model pairs. The Problem Modern LLM deployments are not single-model systems. Three patterns dominate production: Model cascading — route easy queries…
1Key Takeaways
- TL;DR When you swap between different-sized LLMs in production (14B to 32B, for example), the receiving model has to re-run the entire prefill from scratch.
- This paper proposes a closed-form linear mapper that transfers the KV cache across models in the same family, achieving 2.7-25x speedup over re-prefill with 73-98% accuracy retention on most model pairs.
- The Problem Modern LLM deployments are not single-model systems.
- Three patterns dominate production: Model cascading — route easy queries….
2AIWedia Score
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that tL;DR When you swap between different-sized LLMs in production (14B to 32B, for example), the receiving model has to re-run the entire prefill from scratch.
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