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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2310.17157/</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/2310.17157/feed.xml" rel="self" type="application/rss+xml" /><item><title>Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time — mentioned by keivanalizadeh</title><link>https://news.ycombinator.com/item?id=38710824</link><guid isPermaLink="false">urn:hackerpapers:mention:2310.17157:38710824</guid><pubDate>Wed, 20 Dec 2023 17:01:42 GMT</pubDate><description>&lt;p&gt;By the time we put our paper on arxiv this paper was not out so we were not aware of, but it is similar in some ways. Both this paper and us are reliant on our previous paper https://arxiv.org/abs/2310.04564 and dejavu https://arxiv.org/abs/2310.17157. They are targeting limited gpu memory and limit cpu to gpu memory transfer. I don&amp;#x27;t know how it could be useful on Macs because MacBooks have a unified memory and you don&amp;#x27;t need to do that transfer necessarily.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2310.17157/"&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>
