<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>VLLM on 硅基 AGI · 智能体学习与测评</title><link>https://guijiagi.com/tags/vllm/</link><description>Recent content in VLLM on 硅基 AGI · 智能体学习与测评</description><generator>Hugo</generator><language>zh-cn</language><copyright>本站内容采用 CC BY-NC-SA 4.0 国际许可协议授权</copyright><lastBuildDate>Thu, 16 Jul 2026 11:39:00 +0800</lastBuildDate><atom:link href="https://guijiagi.com/tags/vllm/index.xml" rel="self" type="application/rss+xml"/><item><title>大模型推理引擎横评：vLLM、SGLang、TensorRT-LLM</title><link>https://guijiagi.com/posts/b1-f50aea03/</link><pubDate>Thu, 16 Jul 2026 11:39:00 +0800</pubDate><guid>https://guijiagi.com/posts/b1-f50aea03/</guid><description>对比分析三大主流推理引擎的架构设计、性能特征与适用场景，给出选型建议</description></item><item><title>大模型推理部署方案对比：vLLM、SGLang与TensorRT-LLM</title><link>https://guijiagi.com/posts/b2-e0dd1517/</link><pubDate>Thu, 16 Jul 2026 10:38:00 +0800</pubDate><guid>https://guijiagi.com/posts/b2-e0dd1517/</guid><description>深度对比2026年三大主流大模型推理引擎的架构设计、性能表现与适用场景</description></item><item><title>大模型推理优化全景：从KV Cache到投机解码</title><link>https://guijiagi.com/posts/b2-40a7d213/</link><pubDate>Thu, 16 Jul 2026 10:04:00 +0800</pubDate><guid>https://guijiagi.com/posts/b2-40a7d213/</guid><description>系统梳理大模型推理优化技术栈，涵盖KV Cache、注意力优化、量化、投机解码等核心技术</description></item><item><title>大模型推理的Prefix Cache优化：让首token延迟减半</title><link>https://guijiagi.com/posts/article-87/</link><pubDate>Mon, 13 Jul 2026 07:20:00 +0800</pubDate><guid>https://guijiagi.com/posts/article-87/</guid><description>深入解析Prefix Cache的原理、实现与工程优化，大幅降低大模型推理的首token延迟</description></item><item><title>大模型推理的连续批处理技术：吞吐量翻倍的工程艺术</title><link>https://guijiagi.com/posts/article-82/</link><pubDate>Mon, 13 Jul 2026 06:30:00 +0800</pubDate><guid>https://guijiagi.com/posts/article-82/</guid><description>深入解析Continuous Batching的原理、实现与工程优化，理解vLLM/TGI等推理框架的核心技术</description></item><item><title>LLM服务框架对比2026：高性能推理引擎之争</title><link>https://guijiagi.com/posts/llm-serving-framework-2026/</link><pubDate>Thu, 02 Jul 2026 11:37:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-serving-framework-2026/</guid><description>2026年主流LLM服务框架对比，vLLM/TGI/TensorRT-LLM等深度评测</description></item><item><title>vLLM 2026社区进展：高性能推理引擎的进化</title><link>https://guijiagi.com/posts/vllm-2026-community/</link><pubDate>Thu, 02 Jul 2026 11:26:00 +0800</pubDate><guid>https://guijiagi.com/posts/vllm-2026-community/</guid><description>2026年vLLM社区发展动态与技术创新，LLM推理引擎的标杆</description></item><item><title>vLLM Docker部署2026版</title><link>https://guijiagi.com/posts/vllm-docker-deploy-2026/</link><pubDate>Thu, 02 Jul 2026 11:16:00 +0800</pubDate><guid>https://guijiagi.com/posts/vllm-docker-deploy-2026/</guid><description>使用Docker快速部署vLLM推理服务，包含完整的生产配置与优化实践</description></item><item><title>PagedAttention实现细节</title><link>https://guijiagi.com/posts/paged-attention-impl-detail/</link><pubDate>Thu, 02 Jul 2026 11:10:00 +0800</pubDate><guid>https://guijiagi.com/posts/paged-attention-impl-detail/</guid><description>深入解析vLLM中PagedAttention的实现细节，从块管理到内存共享</description></item><item><title>连续批处理内部原理</title><link>https://guijiagi.com/posts/continuous-batching-internals/</link><pubDate>Thu, 02 Jul 2026 11:09:00 +0800</pubDate><guid>https://guijiagi.com/posts/continuous-batching-internals/</guid><description>解析vLLM连续批处理的内部机制，如何动态调度请求提升GPU利用率</description></item><item><title>大模型推理加速 2026：vLLM、SGLang、TensorRT-LLM 深度对比</title><link>https://guijiagi.com/posts/inference-framework-comparison-2026/</link><pubDate>Tue, 30 Jun 2026 17:30:00 +0800</pubDate><guid>https://guijiagi.com/posts/inference-framework-comparison-2026/</guid><description>2026年主流大模型推理框架深度对比：vLLM、SGLang、TensorRT-LLM、TGI的性能、功能与选型指南</description></item><item><title>大模型推理加速2026：vLLM vs SGLang vs TensorRT-LLM</title><link>https://guijiagi.com/posts/llm-inference-speedup-2026/</link><pubDate>Tue, 30 Jun 2026 11:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-inference-speedup-2026/</guid><description>全面对比2026年三大推理引擎vLLM、SGLang和TensorRT-LLM的性能与特性</description></item><item><title>大模型推理优化2026：KV Cache管理前沿方案</title><link>https://guijiagi.com/posts/kv-cache-management-2026-frontier/</link><pubDate>Tue, 30 Jun 2026 09:20:00 +0800</pubDate><guid>https://guijiagi.com/posts/kv-cache-management-2026-frontier/</guid><description>深入分析2026年大模型推理优化的核心方案——KV Cache管理，涵盖压缩、卸载、共享等前沿技术</description></item><item><title>AI推理加速技术2026：量化、剪枝、蒸馏全景</title><link>https://guijiagi.com/posts/ai-inference-optimization-2026/</link><pubDate>Tue, 30 Jun 2026 09:10:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-inference-optimization-2026/</guid><description>2026年大模型推理优化全攻略：FP8/INT4量化、结构化/动态剪枝、知识蒸馏、推测解码、投机采样等核心技术的深度解析与实战对比</description></item><item><title>连续批处理：vLLM 高吞吐推理的核心技术</title><link>https://guijiagi.com/posts/continuous-batching-vllm/</link><pubDate>Sun, 28 Jun 2026 11:08:00 +0800</pubDate><guid>https://guijiagi.com/posts/continuous-batching-vllm/</guid><description>深入解析连续批处理（Continuous Batching）的原理、vLLM实现细节与2026年高吞吐推理系统设计</description></item><item><title>vLLM 2026 生产部署完全指南</title><link>https://guijiagi.com/posts/vllm-2026-deployment-guide/</link><pubDate>Sun, 28 Jun 2026 11:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/vllm-2026-deployment-guide/</guid><description>从单机部署到分布式集群，全面覆盖 vLLM 2026 的安装、配置、优化与生产运维实践</description></item><item><title>Speculative Decoding 实战：推理速度提升 3 倍的配置指南</title><link>https://guijiagi.com/posts/speculative-decoding-practical-3x-speedup-guide/</link><pubDate>Sun, 28 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/speculative-decoding-practical-3x-speedup-guide/</guid><description>Speculative Decoding投机解码实战配置指南：原理详解、Draft模型选择与3倍加速实测</description></item><item><title>大模型推理加速 2026：vLLM vs SGLang vs TensorRT-LLM</title><link>https://guijiagi.com/posts/llm-inference-acceleration-2026-vllm-sglang-tensorrt/</link><pubDate>Sun, 28 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-inference-acceleration-2026-vllm-sglang-tensorrt/</guid><description>三大推理引擎全面对比：vLLM、SGLang、TensorRT-LLM在2026年的性能与功能评测</description></item><item><title>vLLM vs SGLang性能基准</title><link>https://guijiagi.com/posts/vllm-vs-sglang-benchmark/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/vllm-vs-sglang-benchmark/</guid><description>vLLM vs SGLang性能基准</description></item><item><title>vLLM开源推理引擎</title><link>https://guijiagi.com/posts/vllm-opensource-inference/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/vllm-opensource-inference/</guid><description>vLLM开源推理引擎</description></item><item><title>vLLM 生产部署指南：高吞吐推理引擎</title><link>https://guijiagi.com/posts/vllm-production-guide/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/vllm-production-guide/</guid><description>从架构原理到生产部署，全面解析 vLLM 高吞吐推理引擎的核心技术、性能调优与运维方案。</description></item><item><title>本地 LLM 部署选型：Ollama vs vLLM vs LM Studio vs TGI</title><link>https://guijiagi.com/posts/local-llm-deployment/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/local-llm-deployment/</guid><description>全面对比 2026 年四大本地 LLM 部署方案：Ollama、vLLM、LM Studio、TGI 的性能、易用性、并发能力与生产环境适用性分析。</description></item><item><title>连续批处理：vLLM 高吞吐推理的核心技术</title><link>https://guijiagi.com/posts/continuous-batching/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/continuous-batching/</guid><description>深度解析 vLLM 连续批处理（Continuous Batching）如何通过动态请求调度和 PagedAttention 实现高吞吐 LLM 推理服务。</description></item><item><title>SGLang vs vLLM：新一代推理引擎之争</title><link>https://guijiagi.com/posts/sglang-vs-vllm/</link><pubDate>Thu, 25 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/sglang-vs-vllm/</guid><description>从 RadixAttention 到缓存共享，深度对比 SGLang 与 vLLM 的架构差异、性能表现与适用场景。</description></item><item><title>vLLM 部署深度指南：高吞吐 LLM 推理引擎</title><link>https://guijiagi.com/posts/vllm-deployment-deep/</link><pubDate>Thu, 25 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/vllm-deployment-deep/</guid><description>深入解析 vLLM 架构、PagedAttention 原理、Continuous Batching、量化支持与分布式推理部署。</description></item><item><title>vLLM 高级配置指南：压榨每一滴性能</title><link>https://guijiagi.com/posts/vllm-advanced-guide/</link><pubDate>Wed, 24 Jun 2026 16:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/vllm-advanced-guide/</guid><description>深入 vLLM 高级配置，从 PagedAttention 到张量并行、量化推理与 LoRA 动态加载</description></item><item><title>vLLM 部署实战：高吞吐 LLM 推理服务</title><link>https://guijiagi.com/posts/vllm-deployment-guide/</link><pubDate>Wed, 24 Jun 2026 11:10:00 +0800</pubDate><guid>https://guijiagi.com/posts/vllm-deployment-guide/</guid><description>vLLM 的架构原理、部署配置和性能调优全指南</description></item></channel></rss>