English(EN) Why LLM Inference Engines Are Fast: Continuous Batching, PagedAttention and Prefix Caching Explained with vLLM

LLM推理引擎通过连续批处理、PagedAttention和前缀缓存实现高吞吐量

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-20 15:20

像vLLM和SGLang这样的LLM推理引擎的效率，很大程度上得益于超越强大GPU的各种技术。这些引擎采用了连续批处理、PagedAttention和前缀缓存等方法来最大化吞吐量。连续批处理允许动态调度请求，在迭代级别进行处理，而不是等待静态批处理中最长请求的完成。PagedAttention通过使用类似于虚拟内存的基于块的系统来优化KV缓存的内存使用，从而大大减少碎片并增加有效批处理大小。前缀缓存通过重用多个请求中相同的提示前缀先前计算的KV缓存块来进一步提高性能，这项技术对于聊天机器人和检索增强生成等应用尤其有益。 AI

影响 PagedAttention和连续批处理等优化的推理技术对于扩展LLM部署和降低运营成本至关重要。

排序理由对LLM推理优化技术的技术解释。[lever_c_demoted from research: ic=1 ai=1.0]

在 dev.to — LLM tag 阅读 →

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LLM推理引擎通过连续批处理、PagedAttention和前缀缓存实现高吞吐量

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Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 对LLM推理优化技术的技术解释。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

dev.to — LLM tag TIER_1 English(EN) · EME GUG · 2026-10-20 15:20

为什么大型语言模型推理引擎速度快：vLLM 解释了连续批处理、分页注意力与前缀缓存

<p>Tuần này DeepSeek open-source inference engine của họ và lên top Hacker News với gần 1400 điểm. Đọc thread thảo luận, mình thấy nhiều anh em dev vẫn nghĩ chạy LLM nhanh chủ yếu là nhờ GPU mạnh. Thực tế, chênh lệch giữa một script <code>transformers</code> gọi <code>model.gener…

本文如何被排名

报道来源 [1]

为什么大型语言模型推理引擎速度快：vLLM 解释了连续批处理、分页注意力与前缀缓存

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