Verified75% confidenceFactExact time
SGLang achieves efficient KV Cache reuse through innovations such as RadixAttention, and can deliver several-fold throughput improvements when handling complex multi-turn dialogue and Agent Loop scenarios.
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75%
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Long-term
Relevance
5/27/2026
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UnverifiedSGLang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73% similarUnverifiedKV Cache reuse targets the repeated prefix context in multi-turn agent conversations, avoiding recomputing attention on each inference, thereby reducing latency.68% similarVerifiedKV Cache is the persistent storage of intermediate states in the Transformer attention mechanism, recording the key and value vectors for each attention head's processed tokens across all layers, thereby skipping redundant forward propagation computations.67% similarUnverifiedSGLang的RadixAttention对于共享系统提示（system prompt）的场景可带来数倍吞吐量提升67% 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67% similar
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