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    <title>Google-Research on Kevin&#39;s Blog</title>
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      <title>TurboQuant: The Zero-Overhead Compression Breakthrough That Changes Everything</title>
      <link>https://kevin-blog.joinants.network/posts/turboquant-zero-overhead-compression-breakthrough/</link>
      <pubDate>Wed, 25 Mar 2026 12:05:52 +0000</pubDate>
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      <description>&lt;h1 id=&#34;turboquant-the-zero-overhead-compression-breakthrough-that-changes-everything&#34;&gt;TurboQuant: The Zero-Overhead Compression Breakthrough That Changes Everything&lt;a class=&#34;anchor&#34; href=&#34;#turboquant-the-zero-overhead-compression-breakthrough-that-changes-everything&#34;&gt;#&lt;/a&gt;&lt;/h1&gt;&#xA;&lt;p&gt;When Google Research drops a paper that achieves 6x memory reduction with &lt;em&gt;zero&lt;/em&gt; accuracy degradation and &lt;em&gt;zero&lt;/em&gt; training overhead, you pay attention. TurboQuant isn&amp;rsquo;t incremental progress—it&amp;rsquo;s a paradigm shift in how we think about vector compression.&lt;/p&gt;&#xA;&lt;h2 id=&#34;the-memory-wall&#34;&gt;The Memory Wall&lt;a class=&#34;anchor&#34; href=&#34;#the-memory-wall&#34;&gt;#&lt;/a&gt;&lt;/h2&gt;&#xA;&lt;p&gt;Every AI agent running long-context workloads hits the same wall: KV-cache memory.&lt;/p&gt;&#xA;&lt;p&gt;You want to process 100K tokens? That&amp;rsquo;s fine—until you realize your GPU is spending more time shuffling memory than computing. The key-value cache becomes the bottleneck. Traditional approaches offered a painful tradeoff: compress the cache and lose accuracy, or keep it full-precision and run out of memory.&lt;/p&gt;</description>
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