<?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 Route179</title>
    <link>https://route179.dev/tags/inference/</link>
    <description>Recent content in Inference on Route179</description>
    <generator>Hugo</generator>
    <language>en-us</language>
    <copyright>2026 Sheng Chen</copyright>
    <lastBuildDate>Tue, 28 Jul 2026 10:00:00 +1000</lastBuildDate>
    <atom:link href="https://route179.dev/tags/inference/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>Serving DeepSeek-V4-Flash across two DGX Sparks using EKS Hybrid Nodes</title>
      <link>https://route179.dev/2026/07/28/deepseek-v4-flash-dual-dgx-spark-eks-hybrid/</link>
      <pubDate>Tue, 28 Jul 2026 10:00:00 +1000</pubDate>
      <guid>https://route179.dev/2026/07/28/deepseek-v4-flash-dual-dgx-spark-eks-hybrid/</guid>
      <description>&lt;p&gt;In this post, I&amp;rsquo;ll walk you through deploying &lt;strong&gt;&lt;a href=&#34;https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-DSpark&#34;&gt;DeepSeek-V4-Flash-DSpark&lt;/a&gt;&lt;/strong&gt; — a 284B-parameter MoE model (13B active) — across two NVIDIA DGX Sparks running as &lt;strong&gt;EKS Hybrid Nodes&lt;/strong&gt;, connected over a ConnectX-7 RoCE stacking link.&lt;/p&gt;
&lt;p&gt;A single DGX Spark has a 128 GB unified memory pool, so the weights alone (~167 GB) don&amp;rsquo;t fit on one node. Spreading the model across two Sparks with tensor parallelism (TP=2) leaves just enough room for the weights, the KV cache, and framework overhead. I&amp;rsquo;ll show you how to reach &lt;strong&gt;50–60 tok/s&lt;/strong&gt; decode with a &lt;strong&gt;1.79M KV pool&lt;/strong&gt; — a 512K context window at &amp;gt;3× concurrency as deployed, or up to a single 1M context stream.&lt;/p&gt;</description>
    </item>
  </channel>
</rss>
