<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[Topics tagged with gemininano]]></title><description><![CDATA[A list of topics that have been tagged with gemininano]]></description><link>https://board.circlewithadot.net/tags/gemininano</link><generator>RSS for Node</generator><lastBuildDate>Fri, 15 May 2026 03:57:59 GMT</lastBuildDate><atom:link href="https://board.circlewithadot.net/tags/gemininano.rss" rel="self" type="application/rss+xml"/><pubDate>Invalid Date</pubDate><ttl>60</ttl><item><title><![CDATA[#luddites : &quot;WAAH LLMs eat the planet with huge energy hungry Datacentres !!!!&quot;]]></title><description><![CDATA[@bhg @n_dimension Once we dissect the truly colossal power consumption involved in training these beasts,  the staggering inefficiencies of the MAC operation == multiply and accumulate, the Really Big Story is how the indexers are address the power consumption:Algorithm efficiency is a big story right now:  DeepSeek's V3 model reportedly cost just $5.576 million to train and used only around 2,000 chips, where competitors were using 16,000+. As one Rhodium Group analyst put it, DeepSeek "demonstrates that training high-performance models can take far less electricity than previously thought." The catch, as some researchers note, is that cheaper training may just unleash more demand overall:  Jevon's Paradoxhttps://www.axios.com/2025/01/28/deepseek-ai-model-energy-power-demand]]></description><link>https://board.circlewithadot.net/topic/32bba244-d2a3-4a4d-9744-36dedf1c2acd/luddites-waah-llms-eat-the-planet-with-huge-energy-hungry-datacentres</link><guid isPermaLink="true">https://board.circlewithadot.net/topic/32bba244-d2a3-4a4d-9744-36dedf1c2acd/luddites-waah-llms-eat-the-planet-with-huge-energy-hungry-datacentres</guid><dc:creator><![CDATA[tuban_muzuru@beige.party]]></dc:creator><pubDate>Invalid Date</pubDate></item></channel></rss>