<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Sakana AI on The Coders Blog</title><link>https://thecodersblog.com/tag/sakana-ai/</link><description>Recent content in Sakana AI on The Coders Blog</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Mon, 11 May 2026 10:34:14 +0000</lastBuildDate><atom:link href="https://thecodersblog.com/tag/sakana-ai/index.xml" rel="self" type="application/rss+xml"/><item><title>Sakana AI &amp; NVIDIA: TwELL Boosts Inference 20.5% with CUDA</title><link>https://thecodersblog.com/sakana-ai-and-nvidia-s-twell-with-cuda-kernels-2026/</link><pubDate>Mon, 11 May 2026 10:34:14 +0000</pubDate><guid>https://thecodersblog.com/sakana-ai-and-nvidia-s-twell-with-cuda-kernels-2026/</guid><description>&lt;p&gt;You painstakingly prune your state-of-the-art LLM, achieving an astonishing 95% activation sparsity. The theoretical promise of &amp;ldquo;doing less&amp;rdquo; computation whispers of lightning-fast inference and dramatically reduced energy bills. Yet, when you deploy this leaner model to production, the stark reality hits: inference times actually &lt;em&gt;increase&lt;/em&gt;. Profilers reveal an insidious overhead from sparse matrix operations, a frustrating paradox where reducing computation leads to slower execution. This isn&amp;rsquo;t an isolated incident; it&amp;rsquo;s a recurring nightmare for AI engineers chasing efficiency on modern hardware.&lt;/p&gt;</description></item></channel></rss>