<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>NVlabs on SoloSoft</title><link>https://www.solosoft.dev/tags/nvlabs/</link><description>Recent content in NVlabs on SoloSoft</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 01 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://www.solosoft.dev/tags/nvlabs/index.xml" rel="self" type="application/rss+xml"/><item><title>VILA: NVIDIA's Open-Source Vision Language Model Family from NVlabs</title><link>https://www.solosoft.dev/post/vila-vision-language-2026/</link><pubDate>Fri, 01 May 2026 00:00:00 +0000</pubDate><guid>https://www.solosoft.dev/post/vila-vision-language-2026/</guid><description>&lt;p&gt;Vision Language Models (VLMs) that can reason about both images and text have become one of the most active areas in AI research. &lt;strong&gt;VILA&lt;/strong&gt; (Visual Language Model), developed by NVIDIA Labs (NVlabs), represents a comprehensive family of open-source VLMs designed for multi-image reasoning, video understanding, and visual chain-of-thought. The models are designed to scale from edge devices to cloud deployments, making them suitable for robotics, video analytics, and document understanding.&lt;/p&gt;
&lt;p&gt;The VILA family, hosted at &lt;a href="https://github.com/NVlabs/VILA"&gt;github.com/NVlabs/VILA&lt;/a&gt;, has evolved through several generations &amp;ndash; from VILA 1.0 through NVILA and LongVILA &amp;ndash; each introducing new capabilities. VILA models are built on a &amp;ldquo;scale-then-compress&amp;rdquo; philosophy that first trains on high-resolution images to maximize perception quality, then compresses the visual tokens for efficient inference. This approach achieves state-of-the-art results on video understanding benchmarks while remaining practical for deployment.&lt;/p&gt;</description></item></channel></rss>