Unverified50% confidenceFactExact time
The traditional attention mechanism requires pre-allocating contiguous KV Cache memory for each sequence during inference, resulting in severe internal and external fragmentation.
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50%
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Long-term
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10/15/2026
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UnverifiedKV Cache往往占据推理总显存的30%至70%，是制约大模型推理吞吐量和上下文长度的主要瓶颈79% similarVerifiedThe KV Cache (key-value cache) mechanism accelerates inference by caching the Key and Value vectors generated by the self-attention layer to avoid redundant computation, but the longer the context, the more vectors must reside in VRAM, causing memory pressure to grow in a near-linear fashion.78% similarUnverified在多步Agent循环中，中间步骤的错误token会被写入KV Cache并在后续注意力计算中持续影响输出分布，导致错误级联75% similarUnverifiedPlacing the question before the context causes the attention mechanism to assign higher weights to relevant tokens, allowing attention-score-based KV eviction strategies to easily identify important tokens, but in real inference scenarios the question is often given only after the context has been processed.72% similarVerifiedKV Cache将已计算的历史键值矩阵缓存在内存中，将自回归生成的复杂度从随序列长度平方增长降至线性71% similar
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