<?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[Lizard inference engineering: Keep model weights resident]]></title><description><![CDATA[<p dir="auto"><strong>Inference Engineering · Day 1 · Evening</strong></p>
<p dir="auto">Move tokens—not the model.</p>
<p dir="auto">A common local-inference cost is repeated setup and data movement. lizard-native takes a resident-weight approach: load the GGUF weights onto the Direct3D 12 device once, keep them there across calls, and reuse the warmed execution path.</p>
<p dir="auto">That does not make memory limits disappear. It makes residency an explicit planning decision instead of an accidental runtime surprise.</p>
<p dir="auto">The useful question becomes: does the model, cache and current workload fit safely on this device?</p>
<p dir="auto"><strong>Engineering fact:</strong> lizard-native uploads model weights once and keeps them resident on a Direct3D 12 GPU across calls.</p>
<p dir="auto"><a href="https://lizard-llm.qendryx.com/docs.html#lizard-native" rel="nofollow ugc">Read the relevant Lizard page</a></p>
<p dir="auto">#LizardNative #D3D12 #GPUInference #LocalAI #LizardLLM</p>
<p dir="auto">&lt;!-- lizard-marketing-slot:day-01-pm --&gt;</p>
]]></description><link>https://community.lizard-llm.qendryx.com/topic/34/lizard-inference-engineering-keep-model-weights-resident</link><generator>RSS for Node</generator><lastBuildDate>Mon, 24 Aug 2026 01:37:12 GMT</lastBuildDate><atom:link href="https://community.lizard-llm.qendryx.com/topic/34.rss" rel="self" type="application/rss+xml"/><pubDate>Fri, 24 Jul 2026 11:00:07 GMT</pubDate><ttl>60</ttl></channel></rss>