<?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>DeepSeek on Xu'Blog</title><link>https://xuquant.com/en/tags/deepseek/</link><description>Recent content in DeepSeek on Xu'Blog</description><image><title>Xu'Blog</title><url>https://xuquant.com/og-default.png</url><link>https://xuquant.com/og-default.png</link></image><generator>Hugo -- 0.152.2</generator><language>en</language><lastBuildDate>Sat, 13 Sep 2025 10:00:00 +0800</lastBuildDate><atom:link href="https://xuquant.com/en/tags/deepseek/index.xml" rel="self" type="application/rss+xml"/><item><title>Multi-Head Latent Attention: DeepSeek V2/V3 Engineering View</title><link>https://xuquant.com/en/posts/foundation-models/deepseek_series1_mla/</link><pubDate>Sat, 13 Sep 2025 10:00:00 +0800</pubDate><guid>https://xuquant.com/en/posts/foundation-models/deepseek_series1_mla/</guid><description>MLA from the deployment perspective of DeepSeek V2/V3: KV cache compression ratio, inference throughput, engineering comparison with GQA/MQA, and real-world gains under long context. The mathematical derivation is in the companion article.</description></item></channel></rss>