<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Cudnn on Le Site de François</title><link>https://lesitedefrancois.be/en/tags/cudnn/</link><description>Recent content in Cudnn on Le Site de François</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>© 2026 François</copyright><atom:link href="https://lesitedefrancois.be/en/tags/cudnn/index.xml" rel="self" type="application/rss+xml"/><item><title>cuDNN (CUDA Deep Neural Network library)</title><link>https://lesitedefrancois.be/en/ai/cudnn/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://lesitedefrancois.be/en/ai/cudnn/</guid><description>&lt;p&gt;&lt;strong&gt;cuDNN (CUDA Deep Neural Network library)&lt;/strong&gt; is NVIDIA’s library of highly optimized &lt;strong&gt;GPU kernels&lt;/strong&gt; for operations that dominate deep learning: convolutions, matrix multiplies used in attention, pooling, normalization (batch/layer), activations, and recurrent cells. Its objective is to deliver near-peak performance on &lt;strong&gt;CUDA&lt;/strong&gt;-capable GPUs without every framework author hand-writing assembly-tuned kernels. &lt;strong&gt;PyTorch&lt;/strong&gt;, TensorFlow, and many &lt;strong&gt;inference&lt;/strong&gt; engines call cuDNN (directly or via cuBLAS) under the hood for training and serving. cuDNN sits between raw &lt;strong&gt;CUDA&lt;/strong&gt; and application code; version alignment with the CUDA toolkit and driver is mandatory for supported deployments.&lt;/p&gt;</description></item></channel></rss>