Unverified50% confidenceFactExact time
VRAM consumption for context length grows approximately quadratically as length increases, because the attention mechanism must maintain a KV Cache storing the intermediate states of historical tokens
1
Sources
50%
Confidence
Long-term
Relevance
10/7/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedKV Cache reduces latency and computing resource consumption in long-sequence generation by caching the attention key-value pairs of historical tokens, avoiding repeated computation over the entire historical sequence at each generation step.74% similarUnverifiedStuffing a user's operation history from the past weeks or even months wholesale into a context window causes token consumption to grow linearly or even exponentially.68% similarUnverifiedLonger context significantly increases memory usage and inference latency.67% similarUnverified注意力机制计算当前 token 与所有历史 token 相关性的时间复杂度为 O(n²)，其中 n 为序列长度64% similarVerifiedWhen new information conflicts with old memories, the memory layer must be able to identify and update them rather than holding on to outdated knowledge.63% similar
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