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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>SpinQuant: LLM quantization with learned rotations · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2405.16406/</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/2405.16406/feed.xml" rel="self" type="application/rss+xml" /><item><title>SpinQuant: LLM quantization with learned rotations — mentioned by naasking</title><link>https://news.ycombinator.com/item?id=47532762</link><guid isPermaLink="false">urn:hackerpapers:mention:2405.16406:47532762</guid><pubDate>Thu, 26 Mar 2026 16:47:50 GMT</pubDate><description>&lt;p&gt;&amp;gt; Learned rotations for INT4 are cool! Seems similar to SpinQuant? https://arxiv.org/abs/2405.16406 Indeed, but much better! More accurate, less time and space overhead, beats AWQ on almost every bench. I hope it becomes the standard. &amp;gt; In my personal opinion I don’t think the 1.58 bit work is going to make it into the mainstream. I hope you&amp;#x27;re wrong! I&amp;#x27;m more optimistic. Definitely a bit more work to be done, but still very promising. &amp;gt; Being able to natively compute in lower precisions can be a huge performance boost at inference time. ParoQuant is barely worse than FP16. Any less precise…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2405.16406/"&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>SpinQuant: LLM quantization with learned rotations — mentioned by buildbot</title><link>https://news.ycombinator.com/item?id=47531717</link><guid isPermaLink="false">urn:hackerpapers:mention:2405.16406:47531717</guid><pubDate>Thu, 26 Mar 2026 15:28:34 GMT</pubDate><description>&lt;p&gt;Learned rotations for INT4 are cool! Seems similar to SpinQuant? https://arxiv.org/abs/2405.16406 In my personal opinion I don’t think the 1.58 bit work is going to make it into the mainstream. Not sure why you think fractional representations are only useful for training? Being able to natively compute in lower precisions can be a huge performance boost at inference time.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2405.16406/"&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>
