<?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>Llama.cpp 選び方 on Negi AI Lab</title><link>https://ai.negi-lab.com/tags/llama.cpp-%E9%81%B8%E3%81%B3%E6%96%B9/</link><description>Recent content in Llama.cpp 選び方 on Negi AI Lab</description><image><title>Negi AI Lab</title><url>https://ai.negi-lab.com/images/og-default.png</url><link>https://ai.negi-lab.com/images/og-default.png</link></image><generator>Hugo -- 0.154.5</generator><language>ja</language><lastBuildDate>Sun, 25 Oct 2026 04:16:52 +0900</lastBuildDate><atom:link href="https://ai.negi-lab.com/tags/llama.cpp-%E9%81%B8%E3%81%B3%E6%96%B9/index.xml" rel="self" type="application/rss+xml"/><item><title>ローカルLLM用PCおすすめ比較と選び方：自作エンジンよりllama.cppを活かすハードウェア投資術</title><link>https://ai.negi-lab.com/posts/local-llm-hardware-buying-guide-rtx-mac/</link><pubDate>Sun, 25 Oct 2026 00:00:00 +0900</pubDate><guid>https://ai.negi-lab.com/posts/local-llm-hardware-buying-guide-rtx-mac/</guid><description>自前で推論エンジンを作る「Vibecoding」は茨の道。llama.cppやOllamaを前提としたハード選定が実務の最短ルート。。VRAM 16GBが...</description></item></channel></rss>