<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Ganesh C. Sankaran on Blog | Chameleon</title><link>https://blog.chameleoncloud.org/authors/ganesh-c.-sankaran/</link><description>Recent content in Ganesh C. Sankaran on Blog | Chameleon</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Mon, 16 Mar 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://blog.chameleoncloud.org/authors/ganesh-c.-sankaran/index.xml" rel="self" type="application/rss+xml"/><item><title>Running LLMs on Chameleon GPUs from FABRIC via Stitch Ports</title><link>https://blog.chameleoncloud.org/posts/chi-fabric-stitch-ports/</link><pubDate>Mon, 16 Mar 2026 00:00:00 +0000</pubDate><guid>https://blog.chameleoncloud.org/posts/chi-fabric-stitch-ports/</guid><description>&lt;p&gt;What if you could combine Chameleon's bare-metal GPU servers with FABRIC's programmable network fabric — and access the GPU over a private network without ever assigning a public IP? That's exactly what Chameleon's &lt;strong&gt;stitch port&lt;/strong&gt; feature enables, and we've published a &lt;a href="https://trovi.chameleoncloud.org/dashboard/artifacts/9b738237-f9ac-4a4b-9bc5-5f4bebbf9a04"&gt;Trovi artifact that demonstrates the full workflow end to end&lt;/a&gt;.&lt;/p&gt;
&lt;p&gt;The artifact provisions an RTX 6000 GPU server on Chameleon, connects it to a FABRIC slice over &lt;code&gt;fabnetv4&lt;/code&gt;, installs Ollama with a DeepSeek-R1 model on the GPU, and queries the LLM from a FABRIC node — all through the private stitched network. You can use it as-is to run LLM inference, or adapt it as a starting point for your own cross-testbed experiments.&lt;/p&gt;</description></item></channel></rss>