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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2201.11990/</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/2201.11990/feed.xml" rel="self" type="application/rss+xml" /><item><title>Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model — mentioned by gwern</title><link>https://news.ycombinator.com/item?id=31855214</link><guid isPermaLink="false">urn:hackerpapers:mention:2201.11990:31855214</guid><pubDate>Thu, 23 Jun 2022 21:11:50 GMT</pubDate><description>&lt;p&gt;The difference is PaLM was extensively benchmarked and it performed as well as it should, which is to say, amazingly well. The irony here is that you should instead be invoking that other ~500b model, Nvidia&amp;#x27;s Megatron-530b, which was undertrained, only cursorily evaluated (no interest in any new capabilities or even examining old ones like inner monologues) and promptly forgotten by everyone after the headlines about being the largest dense model: https://arxiv.org/abs/2201.11990#microsoftnvidia&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2201.11990/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item></channel></rss>
