Unverified50% confidenceFactExact time
Prefix cache reuse causes KV values to be computed in a different batch than a fresh prefill, so the KV values from a cache hit may differ at the floating-point level from those of a fresh prefill, and the difference propagates through subsequent tokens.
1
Sources
50%
Confidence
Long-term
Relevance
10/18/2026
First Seen
Sources
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Related Claims
UnverifiedKV Cache is a key data structure in autoregressive generation by large models: when generating each new token the model needs the Key and Value vectors of all previous tokens, and if these were recalculated every time the cost would grow quadratically with sequence length.76% similarUnverifiedWhen generating each token, a Transformer model needs to compute the Key and Value vectors of all previous tokens, and these tensors are collectively called the KV cache.74% similarPartially VerifiedThe KV cache is a core data structure in Transformer autoregressive inference, and its size is approximately 2 × number of layers × number of attention heads × head dimension × sequence length × precision bytes.73% similarUnverifiedOperational tuning experience in areas such as KV Cache management, batch scheduling, and VRAM allocation is almost entirely decoupled from specific model versions.72% similarUnverifiedIf two requests share the same prefix content, the system can reuse the previously computed KV vectors, skipping the repeated forward propagation computation72% similar
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