<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>GraphDB on JAVAPRO International</title><link>https://javapro-en.svenruppert.com/tags/graphdb/</link><description>Recent content in GraphDB on JAVAPRO International</description><generator>Hugo</generator><language>en-US</language><lastBuildDate>Wed, 15 Apr 2026 07:00:01 +0000</lastBuildDate><atom:link href="https://javapro-en.svenruppert.com/tags/graphdb/index.xml" rel="self" type="application/rss+xml"/><item><title>Petabyte-Scale AI Memory with Serverless Java</title><link>https://javapro-en.svenruppert.com/petabyte-scale-ai-memory-with-serverless-java/</link><pubDate>Wed, 15 Apr 2026 07:00:01 +0000</pubDate><guid>https://javapro-en.svenruppert.com/petabyte-scale-ai-memory-with-serverless-java/</guid><description>&lt;p&gt;&lt;a href="https://youtu.be/KfXn6v_DmTo"&gt;&lt;figure class="post-figure"&gt;
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&lt;p&gt;The current wave of Generative AI innovation is built on a paradox. While models are becoming more powerful and efficient, the infrastructure required to operate them at scale is becoming increasingly wasteful. Enterprises are investing millions into compute, storage, and energy, yet a significant portion of these resources remains idle. Modern vector databases, graph systems, and caching layers are based on a monolithic architecture run as always-on clusters, consuming CPU and RAM regardless of whether data is actively accessed. Studies and industry analyses consistently show that up to 80% of compute resources in such systems are effectively wasted on idle workloads.&lt;/p&gt;</description></item></channel></rss>