@somi_ai: If this holds up, you stop picking one model per app. Cheap model does the boring turns. Hard turn comes up, you hand t…
Summary
NVIDIA's paper introduces a method to transfer KV cache between AI models, allowing target models to skip prefill entirely and achieve 2.7 to 25x faster conversions, which could improve efficiency in multi-model applications.
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Cached at: 09/09/26, 11:51 AM
If this holds up, you stop picking one model per app.
Cheap model does the boring turns. Hard turn comes up, you hand the whole conversation to a big model and it doesn’t re-read any of it. That handoff costs a full prefill today, which is why nobody bothers.
I want to see it on a long conversation before I believe it.
Akshay 🚀 (@akshay_pachaar): Brilliant paper by NVIDIA.
they found a way to make KV cache transferable between models.
the target model skips prefill entirely, and the conversion runs 2.7 to 25x faster than processing the context again.
let’s understand why this is so important today.
LLM APIs are
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