<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Turboquant on danilchenko.dev</title><link>https://www.danilchenko.dev/tags/turboquant/</link><description>Recent content in Turboquant on danilchenko.dev</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 27 Mar 2026 06:00:00 +0000</lastBuildDate><atom:link href="https://www.danilchenko.dev/tags/turboquant/index.xml" rel="self" type="application/rss+xml"/><item><title>Google's TurboQuant Compresses LLM Memory 6x With Zero Accuracy Loss — Here's How It Works</title><link>https://www.danilchenko.dev/posts/2026-03-27-google-turboquant-llm-compression-6x-zero-accuracy-loss/</link><pubDate>Fri, 27 Mar 2026 06:00:00 +0000</pubDate><guid>https://www.danilchenko.dev/posts/2026-03-27-google-turboquant-llm-compression-6x-zero-accuracy-loss/</guid><description>Google&amp;#39;s TurboQuant algorithm compresses LLM KV cache memory by 6x with zero accuracy loss and no retraining needed. We break down the ICLR 2026 paper.</description></item></channel></rss>