<?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 🌲Treetopia🌲</title>
    <link>https://tree2601.github.io/en/tags/deepseek/</link>
    <description>Recent content in DeepSeek on 🌲Treetopia🌲</description>
    <generator>Hugo -- 0.154.2</generator>
    <language>en</language>
    <lastBuildDate>Tue, 06 Jan 2026 11:16:30 +0800</lastBuildDate>
    <atom:link href="https://tree2601.github.io/en/tags/deepseek/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>DeepSeek-671B Distributed Deployment</title>
      <link>https://tree2601.github.io/en/posts/2026/deepseek-671b/</link>
      <pubDate>Tue, 06 Jan 2026 11:16:30 +0800</pubDate>
      <guid>https://tree2601.github.io/en/posts/2026/deepseek-671b/</guid>
      <description>&lt;h3 id=&#34;1-overview&#34;&gt;1. Overview&lt;/h3&gt;
&lt;p&gt;a. This guide describes the deployment of the DeepSeek-671B model across two servers, each equipped with 8x NVIDIA L20 GPUs. The technology stack utilizes Docker for containerization, the vLLM high-performance inference engine, and the Ray distributed computing framework.&lt;/p&gt;
&lt;p&gt;b. Official Documentation: &lt;a href=&#34;https://docs.vllm.ai/en/v0.8.1/serving/distributed_serving.html&#34;&gt;vLLM-Distributed&lt;/a&gt;&lt;/p&gt;
&lt;p&gt;c. The official tutorial involves complex steps requiring frequent switching between multiple SSH sessions. To simplify the process, this article consolidates and optimizes the official workflow into a systematic, one-stop deployment guide.&lt;/p&gt;</description>
    </item>
  </channel>
</rss>
