<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[NUMA aware inference with lk_moe / lvllm / lsglang]]></title><description><![CDATA[<h3>Links:</h3>
<p dir="auto"><a href="https://pypi.org/project/lk-moe/" rel="nofollow ugc">lk-moe</a><br />
<a href="https://github.com/guqiong96/Lvllm" rel="nofollow ugc">Lvllm</a><br />
<a href="https://github.com/guqiong96/Lsglang" rel="nofollow ugc">Lsglang</a></p>
<p dir="auto">Open source implementation of lk-moe: <a href="https://github.com/yeungtuzi/xiaotu-moe" rel="nofollow ugc">xiaotu-moe</a></p>
<p dir="auto">These inference engines seem to implement features that greatly improve CPU and CPU offload performance with the NUMA parallel architecture, still to be tested, would appreciate benchmarks! (There are some numbers in the GitHub repos.)</p>
<div class="airigs-disclosure airigs-disclosure--no" data-ai-disclosure="no"><i class="fa fa-user" aria-hidden="true"></i><span class="airigs-disclosure__label">No AI used</span></div>]]></description><link>https://airigs.net/topic/5/numa-aware-inference-with-lk_moe-lvllm-lsglang</link><generator>RSS for Node</generator><lastBuildDate>Mon, 02 Nov 2026 07:03:07 GMT</lastBuildDate><atom:link href="https://airigs.net/topic/5.rss" rel="self" type="application/rss+xml"/><pubDate>Mon, 28 Sep 2026 16:25:26 GMT</pubDate><ttl>60</ttl></channel></rss>