English(EN) Demystifying LLM Serving Infrastructure: How PagedAttention and Continuous Batching Scale Inference

PagedAttention和连续批处理革新大语言模型推理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-04 20:40

大语言模型（LLM）服务基础设施在扩展推理方面面临严峻挑战，主要源于内存带宽和容量限制，而非原始计算能力。PagedAttention和连续批处理等关键创新，由vLLM等引擎率先推出，通过优化KV缓存的管理来解决这些问题。PagedAttention借鉴了操作系统虚拟内存的理念，将KV缓存划分为更小的块，将内存浪费从60%以上降低到4%以下，并提高了并发性。连续批处理通过在迭代级别处理请求，进一步提高了效率，避免了静态批处理和长时间等待请求导致的GPU空闲。 AI

影响这些基础设施的改进显著提高了LLM推理效率，实现了更高的吞吐量，并降低了部署大型模型的硬件成本。

排序理由该条目详细介绍了LLM服务基础设施的技术创新，解释了PagedAttention和连续批处理等概念。[lever_c_demoted from research: ic=1 ai=1.0]

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dev.to — LLM tag TIER_1 English(EN) · Ahmed Adawy · 2026-10-04 20:40

揭秘大语言模型服务基础设施：PagedAttention 和连续批处理如何扩展推理

<p>Demystifying LLM Serving Infrastructure: How PagedAttention and Continuous Batching Scale Inference<br /> Moving a Large Language Model (LLM) from a local prototype in a Jupyter notebook to a high-throughput, multi-tenant production environment is a brutal awakening. While dat…

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揭秘大语言模型服务基础设施：PagedAttention 和连续批处理如何扩展推理

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