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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>FP8 Quantization: The Power of the Exponent · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2208.09225/</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/2208.09225/feed.xml" rel="self" type="application/rss+xml" /><item><title>FP8 Quantization: The Power of the Exponent — mentioned by mlgoatherder</title><link>https://news.ycombinator.com/item?id=35394006</link><guid isPermaLink="false">urn:hackerpapers:mention:2208.09225:35394006</guid><pubDate>Fri, 31 Mar 2023 21:37:30 GMT</pubDate><description>&lt;p&gt;I&amp;#x27;ve done some experiments here with Llama 13B, in my subjective experience the original fp16 model is significantly better (particularly on coding tasks). There are a bunch of synthetic benchmarks such a wikitext2 PPL and all the whiz bang quantization schemes seem to score well but subjectively something is missing. I&amp;#x27;ve been able to compare 4 bit GPTQ, naive int8, LLM.int8, fp16, and fp32. LLM.int8 does impressively well but inference is 4-5x slower than native fp16. Oddly I recently ran a fork of the model on the ONNX runtime, I&amp;#x27;m convinced that the model performed better than…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2208.09225/"&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>
