<?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>Lei Mao | AI Inference &amp; Market</title><link>https://maomao123321.github.io/</link><description>Recent content on Lei Mao | AI Inference &amp; Market</description><generator>Hugo -- 0.146.0</generator><language>en</language><lastBuildDate>Sun, 07 Dec 2025 13:06:53 -0500</lastBuildDate><atom:link href="https://maomao123321.github.io/index.xml" rel="self" type="application/rss+xml"/><item><title>The KV Cache Dilemma: Why LLM Inference Needs to "Forget" to Scale?</title><link>https://maomao123321.github.io/inference/kv-cache-dilemma/</link><pubDate>Sun, 07 Dec 2025 13:06:53 -0500</pubDate><guid>https://maomao123321.github.io/inference/kv-cache-dilemma/</guid><description>&lt;p>Have you ever wondered why the 10th turn of a conversation with an LLM feels just as fast as the first? Mathematically, this shouldn&amp;rsquo;t happen. As the context grows (History + New Question), the computation required to generate the next token should increase linearly. The secret sauce is KV Cache (Key-Value Cache). It is the single most critical component in LLM Inference optimization.&lt;/p></description></item><item><title/><link>https://maomao123321.github.io/about/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://maomao123321.github.io/about/</guid><description/></item></channel></rss>