<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Amd on johanneskueber.com</title><link>https://johanneskueber.com/tags/amd/</link><description>Recent content in Amd on johanneskueber.com</description><generator>Hugo</generator><language>en_US</language><lastBuildDate>Mon, 24 Jun 2024 07:06:26 +0000</lastBuildDate><atom:link href="https://johanneskueber.com/tags/amd/index.xml" rel="self" type="application/rss+xml"/><item><title>Enable hardware acceleration for Jellyfin in Kubernetes - AMD Edition</title><link>https://johanneskueber.com/posts/2024-06-24-hardware-acceleration-kubernetes-jellyfin/</link><pubDate>Mon, 24 Jun 2024 07:06:26 +0000</pubDate><guid>https://johanneskueber.com/posts/2024-06-24-hardware-acceleration-kubernetes-jellyfin/</guid><description>&lt;p&gt;&lt;a href="https://jellyfin.org/"&gt;Jellyfin&lt;/a&gt; is an open-source media server software that allows users to manage and stream their personal collection of movies, TV shows, music, and other media files. It is designed as an alternative to proprietary media server solutions like Plex and Emby, offering similar functionality but without any licensing costs or restrictions.&lt;/p&gt;
&lt;p&gt;Jellyfin is running in my bare-metal kubernetes cluster. However, running the container without additional configuraion only gives Jellyfin access to the CPU for decoding video streams. If a GPU is available it would be better to use the GPU as it - most of the time - also has hardware acceleration for well-known codecs and in addition it takes some work off the CPU. In my case, the Athlon 3000G has an on-board Vega 3 graphics chip. Now the only thing left to do is to give Jellyfin access to the GPU.&lt;/p&gt;</description></item></channel></rss>