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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Power Hungry Processing: Watts Driving the Cost of AI Deployment? · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2311.16863/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2311.16863/feed.xml" rel="self" type="application/rss+xml" /><item><title>Power Hungry Processing: Watts Driving the Cost of AI Deployment? — HN submission</title><link>https://news.ycombinator.com/item?id=40029327</link><guid isPermaLink="false">urn:hackerpapers:submission:2311.16863:40029327</guid><pubDate>Sun, 14 Apr 2024 07:23:52 GMT</pubDate><description>&lt;p&gt;Power Hungry Processing: Watts Driving the Cost of AI Deployment?&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;2 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2311.16863/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Power Hungry Processing: Watts Driving the Cost of AI Deployment? — mentioned by colesantiago</title><link>https://news.ycombinator.com/item?id=39915746</link><guid isPermaLink="false">urn:hackerpapers:mention:2311.16863:39915746</guid><pubDate>Wed, 03 Apr 2024 10:49:59 GMT</pubDate><description>&lt;p&gt;They are both the same. Both have breathless hype. Both are extremely overvalued and are a solution looking for a problem. Both aren&amp;#x27;t really useful unless you&amp;#x27;re (trying to count generating cat memes and broken hallucinating code a use case, it isn&amp;#x27;t). And last and the most important of all: Both of these waste a ton of energy either by training or running the software and are unlikely to make startups money due to it&amp;#x27;s rapid commoditization. https://arxiv.org/pdf/2311.16863.pdf https://www.theverge.com/24066646/ai-electricity-energy-watt...&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2311.16863/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Power Hungry Processing: Watts Driving the Cost of AI Deployment? — mentioned by tomjohnneill</title><link>https://news.ycombinator.com/item?id=39480563</link><guid isPermaLink="false">urn:hackerpapers:mention:2311.16863:39480563</guid><pubDate>Fri, 23 Feb 2024 13:53:42 GMT</pubDate><description>&lt;p&gt;I can definitely imagine they&amp;#x27;re not covering the amortised cost of the training with the cost per individual inference request. It seems less likely to me that they&amp;#x27;re making a significant loss on each subsequent request, but again no source from me on that either. Looking a bit more into this, I found this paper: https://arxiv.org/pdf/2311.16863.pdf. It references a table saying that text generation uses 0.047 kWh per 1000 inferences, which is 1-2 orders of magnitude lower than my estimate. Though that is for GPT2, so possibly tracks to something roughly in the ~0.001 kWh per inference for…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2311.16863/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Power Hungry Processing: Watts Driving the Cost of AI Deployment? — mentioned by Baldbvrhunter</title><link>https://news.ycombinator.com/item?id=38719064</link><guid isPermaLink="false">urn:hackerpapers:mention:2311.16863:38719064</guid><pubDate>Thu, 21 Dec 2023 11:06:51 GMT</pubDate><description>&lt;p&gt;The actual paper [0] says approx 4860 joules per image, so 70 images is the same as boiling a litre of water. Charging a phone takes 20k joules. [0] https://arxiv.org/pdf/2311.16863.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2311.16863/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
