Verified65% confidenceFactExact time
KV Cache 是 Transformer 注意力机制中间态的持久化存储，记录每层注意力头对已处理token的键和值向量，从而跳过重复的前向传播计算
3
Sources
65%
Confidence
Long-term
Relevance
9/4/2026
First Seen
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UnverifiedIn the Transformer architecture, generating each new token requires attention computation over all previous tokens, and KV Cache avoids redundant computation by caching previously computed key-value pairs.77% similarUnverifiedTransformer 架构中自注意力层会为每个 Token 生成对应的 Key 和 Value 向量并缓存在 GPU 显存中，即 KV Cache 机制，上下文越长占用显存越大，推理成本呈近线性增长76% similarUnverifiedTransformer架构的自回归生成机制导致输入Token只需一次前向传播编码并以KV Cache存储复用，而输出Token每一步生成都需完整前向传播，推理成本随输出长度线性增长68% similarUnverified提示词缓存依赖Transformer的KV缓存机制，通过复用相同前缀已计算的KV向量跳过重复计算，节省GPU算力并降低首Token延迟68% similarUnverifiedIn traditional Transformer architectures, as sequence length increases, both floating-point operations for attention computation and KV Cache memory usage grow linearly or even super-linearly.67% similar
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