<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[8-bit KV Cache on Ampere]]></title><description><![CDATA[<p dir="auto">When using vLLM on Ampere GPUs, you may see that when selecting 8bit quantization formats for kv cache (fp8, int8) it falls back to other inference engines (Flashinfer, Triton Attention).</p>
<p dir="auto">In my experience, Triton Attention performs very poorly on Ampere, especially on higher contexts, and Flashinfer doesn't work with DFlash2, which is most likely why these alternatives were made: (both available in the <a href="https://github.com/syv-ai/HyperQwen" rel="nofollow ugc">HyperQwen</a> repository).</p>
<ol>
<li>For FP8 KV Cache -&gt; There's a fork of Flash Attention 2 with fp8 kv cache.</li>
<li>For Int8 KV Cache -&gt; Use the 'PTH' option in HyperQwen.</li>
</ol>
<p dir="auto">In my experience, FP8 performed better at prefill and Int8 performed better at decode, both work with DFlash2 with solid results.</p>
<div class="airigs-disclosure airigs-disclosure--no" data-ai-disclosure="no"><i class="fa fa-user" aria-hidden="true"></i><span class="airigs-disclosure__label">No AI used</span></div>]]></description><link>https://airigs.net/topic/39/8-bit-kv-cache-on-ampere</link><generator>RSS for Node</generator><lastBuildDate>Mon, 02 Nov 2026 05:41:10 GMT</lastBuildDate><atom:link href="https://airigs.net/topic/39.rss" rel="self" type="application/rss+xml"/><pubDate>Sat, 31 Oct 2026 23:48:20 GMT</pubDate><ttl>60</ttl></channel></rss>