Unverified50% confidenceFactExact time
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
1
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50%
Confidence
Long-term
Relevance
9/9/2026
First Seen
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UnverifiedTransformer 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80% 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76% similarUnverifiedGPU并行归约操作的浮点累加顺序不固定导致传统Transformer注意力机制的实际输出存在微小的不可复现波动74% similarUnverifiedIn 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.70% similarUnverifiedSGLang通过RadixAttention实现基于哈希的前缀缓存机制，命中缓存则直接复用已有KV张量并跳过prefill阶段重复计算69% similar
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