<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Inference on StorageNews</title><link>https://storagenews.top/tags/inference/</link><description>Recent content in Inference on StorageNews</description><generator>Hugo</generator><language>en</language><lastBuildDate>Thu, 19 Feb 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://storagenews.top/tags/inference/index.xml" rel="self" type="application/rss+xml"/><item><title>Data readiness bottlenecks: Why AI stalls</title><link>https://storagenews.top/posts/data-readiness-bottlenecks-why-ai-stalls/</link><pubDate>Thu, 19 Feb 2026 00:00:00 +0000</pubDate><guid>https://storagenews.top/posts/data-readiness-bottlenecks-why-ai-stalls/</guid><description>&lt;meta charset="utf-8">
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&lt;p class="std-text">Early AI projects wasted 80 percent of budgets on compute while treating storage as an afterthought, a miscalculation HPE Storage leadership identifies as the primary cause of current production failures. The era of ignoring infrastructure constraints is over; &lt;strong>data readiness&lt;/strong> has officially replaced model size as the critical bottleneck for enterprise artificial intelligence. As organizations attempt to scale beyond proof-of-concept trials, they are discovering that raw GPU power cannot compensate for fragmented, uncurated data ecosystems that choke &lt;strong>inference pipelines&lt;/strong>.&lt;/p></description></item></channel></rss>