Unverified50% confidenceFactExact time
KV Cache is a mechanism in which the Transformer decoder, during autoregressive generation, caches the already-computed Key and Value matrices of each layer, reducing the per-step attention computation from O(n²) to O(n).
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Long-term
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10/19/2026
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VerifiedKV Cache reduces inference complexity from O(n²) to O(n) by caching the already-computed Key/Value matrices.79% similarUnverifiedIn large language model inference, the Transformer's self-attention mechanism needs to store Key and Value vectors for each processed token, and this batch of storage is the KV Cache.79% similarUnverifiedKV Cache is a core optimization technique during the inference phase of large language models, essentially a space-for-time strategy that avoids redundant computation by caching the Key and Value vectors of historical tokens.78% similarVerifiedThe KV Cache mechanism can cache repeated prefix content, and the more concise the system prompt, the higher the cache hit rate.76% similarUnverifiedProcessing input can spread out computational costs through mechanisms such as KV Cache, whereas generating each output token requires a full forward inference pass.74% similar
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