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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Quantization-Aware Distillation for NVFP4 Inference Accuracy Recovery · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2601.20088/</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/2601.20088/feed.xml" rel="self" type="application/rss+xml" /><item><title>Quantization-Aware Distillation for NVFP4 Inference Accuracy Recovery — mentioned by woadwarrior01</title><link>https://news.ycombinator.com/item?id=47976247</link><guid isPermaLink="false">urn:hackerpapers:mention:2601.20088:47976247</guid><pubDate>Fri, 01 May 2026 15:54:35 GMT</pubDate><description>&lt;p&gt;&amp;gt; at Q4_K_M, stock-style quantization is retaining ~99–99.8% of BF16 accuracy That&amp;#x27;s a tall claim. By that measure, even NVIDIA&amp;#x27;s QAD, which is AFAIK is currently SOTA for 4-bit quantization (albeit NVFP4 instead of INT4) would be worse than Q4_K_M RTN quantization. :D https://arxiv.org/abs/2601.20088&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2601.20088/"&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>
