<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>VLLM on C.CUI's Log</title><link>https://cuicaihao.github.io/tags/vllm/</link><description>Recent content in VLLM on C.CUI's Log</description><generator>Hugo</generator><language>en-AU</language><lastBuildDate>Sat, 08 Aug 2026 07:00:00 +1000</lastBuildDate><atom:link href="https://cuicaihao.github.io/tags/vllm/index.xml" rel="self" type="application/rss+xml"/><item><title>Optimizing LLM Inference: From PagedAttention to AI Infra</title><link>https://cuicaihao.github.io/posts/2026-08-08-optimizing-llm-inference-the-role-of-pagedattention-in-efficient-gpu-memory-management/</link><pubDate>Sat, 08 Aug 2026 07:00:00 +1000</pubDate><guid>https://cuicaihao.github.io/posts/2026-08-08-optimizing-llm-inference-the-role-of-pagedattention-in-efficient-gpu-memory-management/</guid><description>Why do expensive GPUs remain underutilized during LLM inference? This post explores how vLLM&amp;rsquo;s PagedAttention leverages operating system virtual memory concepts, alongside continuous batching, RadixAttention, and Prefill-Decode disaggregation, to reshape modern large language model serving.</description></item></channel></rss>