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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>LUT-LLM: Efficient Large Language Model Inference with Memory-based Computations on FPGAs · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2511.06174/</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/2511.06174/feed.xml" rel="self" type="application/rss+xml" /><item><title>LUT-LLM: Efficient Large Language Model Inference with Memory-based Computations on FPGAs — mentioned by pankajdoharey</title><link>https://news.ycombinator.com/item?id=47098391</link><guid isPermaLink="false">urn:hackerpapers:mention:2511.06174:47098391</guid><pubDate>Sat, 21 Feb 2026 07:30:34 GMT</pubDate><description>&lt;p&gt;There is nothing new here. This has been demonstrated several times by previous researchers: https://arxiv.org/abs/2511.06174 https://arxiv.org/abs/2401.03868 For a real world use case, you would need an FPGA with terabytes of RAM. Perhaps it&amp;#x27;ll be a Off chip HBM. But for s large models, even that won&amp;#x27;t be enough. Then you would need to figure out NV-link like interconnect for these FPGAs. And we are back to square one.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2511.06174/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AR</category></item></channel></rss>
