<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Gpu on cloudmato.com</title><link>https://cloudmato.com/tags/gpu/</link><description>Recent content in Gpu on cloudmato.com</description><generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>cloudmato.com</managingEditor><webMaster>cloudmato.com</webMaster><lastBuildDate>Sun, 14 Jun 2026 21:01:45 +0530</lastBuildDate><atom:link href="https://cloudmato.com/tags/gpu/index.xml" rel="self" type="application/rss+xml"/><item><title>How OpenAI and Anthropic Actually Train Their Models</title><link>https://cloudmato.com/posts/how-openai-anthropic-train-models/</link><pubDate>Sun, 14 Jun 2026 21:01:45 +0530</pubDate><author>cloudmato.com</author><guid>https://cloudmato.com/posts/how-openai-anthropic-train-models/</guid><description>&lt;p&gt;Everyone talks about ChatGPT and Claude like they just appeared one day. You type something, you get an answer, magic. But have you ever stopped to ask what it actually takes to &lt;em&gt;make&lt;/em&gt; one of these things? Not the chat interface — the model itself. The thing that took months, hundreds of millions of dollars, and enough electricity to power a small town.&lt;/p&gt;
&lt;p&gt;I&amp;rsquo;ve been curious about this for a while, partly because the numbers are genuinely hard to believe until you sit with them. So I went digging through what&amp;rsquo;s actually known — the leaked architecture details, the hardware announcements, the data center buildouts. Some of it is public, some of it is well-sourced speculation, and some of it the labs keep deliberately vague. Let me walk you through what we actually know.&lt;/p&gt;</description></item></channel></rss>