<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Ai Chips on MacWorks</title><link>https://macworks.dev/tags/ai-chips/</link><description>Recent content in Ai Chips on MacWorks</description><generator>Hugo</generator><language>en</language><atom:link href="https://macworks.dev/tags/ai-chips/index.xml" rel="self" type="application/rss+xml"/><item><title>2026-07-08</title><link>https://macworks.dev/docs/week/tech_news_cn/tech-news-cn-2026-07-08/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://macworks.dev/docs/week/tech_news_cn/tech-news-cn-2026-07-08/</guid><description>&lt;h1 id="chinese-tech-daily--2026-07-08"&gt;Chinese Tech Daily — 2026-07-08&lt;a class="anchor" href="#chinese-tech-daily--2026-07-08"&gt;#&lt;/a&gt;&lt;/h1&gt;
&lt;h2 id="top-story"&gt;Top Story&lt;a class="anchor" href="#top-story"&gt;#&lt;/a&gt;&lt;/h2&gt;
&lt;p&gt;DeepSeek has reportedly initiated the development of its own AI inference chips, a project that began roughly a year ago. Unlike training accelerators, this bespoke silicon is designed specifically to handle user requests and generative workloads, aiming to reduce DeepSeek&amp;rsquo;s reliance on hardware from Nvidia and Huawei. By controlling the hardware layer, DeepSeek hopes to co-design its chips with its model architectures—such as its Mixture of Experts (MoE) structure—to drastically lower inference costs as its global token volume continues to scale.&lt;/p&gt;</description></item></channel></rss>