<?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>Inference on Yi's Personal Blog</title><link>https://yiliu30.github.io/tags/inference/</link><description>Recent content in Inference on Yi's Personal Blog</description><generator>Hugo -- 0.146.0</generator><language>en-us</language><lastBuildDate>Sun, 26 Apr 2026 10:00:00 +0000</lastBuildDate><atom:link href="https://yiliu30.github.io/tags/inference/index.xml" rel="self" type="application/rss+xml"/><item><title>DeepSeek V4 KV Cache Design: How 1M Tokens Fit in 10 GiB</title><link>https://yiliu30.github.io/posts/ds-v4/</link><pubDate>Sun, 26 Apr 2026 10:00:00 +0000</pubDate><guid>https://yiliu30.github.io/posts/ds-v4/</guid><description>&lt;p>DeepSeek V4 supports 1M-token context, yet its KV cache for a 61-layer model
fits in ~9.6 GiB (BF16) — a &lt;strong>6.3× reduction&lt;/strong> over naive full attention. This
post breaks down how three orthogonal techniques combine to make that possible.&lt;/p></description></item></channel></rss>