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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>FP8-LM: Training FP8 Large Language Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2310.18313/</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/2310.18313/feed.xml" rel="self" type="application/rss+xml" /><item><title>FP8-LM: Training FP8 Large Language Models — mentioned by YetAnotherNick</title><link>https://news.ycombinator.com/item?id=43598934</link><guid isPermaLink="false">urn:hackerpapers:mention:2310.18313:43598934</guid><pubDate>Sun, 06 Apr 2025 04:19:18 GMT</pubDate><description>&lt;p&gt;It&amp;#x27;s not just scale. Even for single GPU, it is hard to acheive 2x speed improvement as the GPU specs states. Even NVIDIA&amp;#x27;s own Tensor Engine acheives 28% extra FLOP/s[1]. [1]: https://arxiv.org/pdf/2310.18313&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2310.18313/"&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>FP8-LM: Training FP8 Large Language Models — HN submission</title><link>https://news.ycombinator.com/item?id=38077071</link><guid isPermaLink="false">urn:hackerpapers:submission:2310.18313:38077071</guid><pubDate>Mon, 30 Oct 2023 22:53:10 GMT</pubDate><description>&lt;p&gt;Training LLMs using 8-bit numbers&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;1 point; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2310.18313/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
