<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>MoE on English AI Terms Dictionary</title><link>https://terms-en.ai-term-hub.com/en/tags/moe/</link><description>Recent content in MoE on English AI Terms Dictionary</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 18 Jul 2026 11:44:44 +0000</lastBuildDate><atom:link href="https://terms-en.ai-term-hub.com/en/tags/moe/index.xml" rel="self" type="application/rss+xml"/><item><title>DeepSeek V3</title><link>https://terms-en.ai-term-hub.com/en/terms/deepseek_v3/</link><pubDate>Sat, 18 Jul 2026 09:55:14 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/deepseek_v3/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>DeepSeek V3 is an advanced iteration in the DeepSeek model family, characterized by its dense activation of only a small subset of parameters during inference via Mixture-of-Experts routing. This architecture allows it to scale up parameter count dramatically while keeping computational costs manageable. It demonstrates exceptional proficiency in mathematics, coding, and logical reasoning, often outperforming larger dense models. The model was trained using a novel hybrid optimization strategy and extensive high-quality data, making it a leading choice for developers seeking high-performance open-source LLMs for complex task execution.&lt;/p></description></item></channel></rss>