<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>cuda on toorun.dev</title><link>https://toorun.dev/tags/cuda/</link><description>Recent content in cuda on toorun.dev</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><lastBuildDate>Wed, 02 Sep 2026 11:00:00 +0000</lastBuildDate><atom:link href="https://toorun.dev/tags/cuda/index.xml" rel="self" type="application/rss+xml"/><item><title>CUDA and NPU Explained: GPU Acceleration, AI Inference, and Edge Performance for Embedded and Data Center Systems</title><link>https://toorun.dev/posts/cuda-and-npu-explained-gpu-acceleration-ai-inference-embedded-systems/</link><pubDate>Wed, 02 Sep 2026 11:00:00 +0000</pubDate><guid>https://toorun.dev/posts/cuda-and-npu-explained-gpu-acceleration-ai-inference-embedded-systems/</guid><description>CUDA and NPU Explained: GPU Acceleration, AI Inference, and Edge Performance for Embedded and Data Center Systems AI workloads are not handled well by a normal CPU alone. Training and inference need parallel compute. That is where GPUs and NPUs come in.
This guide explains the difference between CUDA and NPUs in practical terms, when to use each one, and how they fit into embedded and data center systems.
What Is CUDA?</description></item></channel></rss>