<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Hardware on Chengyu Wang</title><link>https://chengyu.eu/tags/hardware/</link><description>Recent content in Hardware on Chengyu Wang</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>© 2026 Chengyu</copyright><lastBuildDate>Thu, 26 Feb 2026 06:00:06 +0000</lastBuildDate><atom:link href="https://chengyu.eu/tags/hardware/index.xml" rel="self" type="application/rss+xml"/><item><title>NVIDIA's Blackwell Architecture, Explained</title><link>https://chengyu.eu/posts/nvidia-blackwell-architecture/</link><pubDate>Thu, 26 Feb 2026 06:00:06 +0000</pubDate><guid>https://chengyu.eu/posts/nvidia-blackwell-architecture/</guid><description>Two full-reticle dies fused into one GPU, FP4 inference, and a fifth-generation NVLink that connects up to 576 GPUs — the architecture behind NVIDIA&amp;rsquo;s &amp;lsquo;AI factory&amp;rsquo; strategy.</description></item><item><title>TPU vs. GPU: A Deep Dive, and Who's Actually Using TPUs</title><link>https://chengyu.eu/posts/tpu-vs-gpu-deep-dive/</link><pubDate>Sun, 18 Jan 2026 17:00:08 +0000</pubDate><guid>https://chengyu.eu/posts/tpu-vs-gpu-deep-dive/</guid><description>GPUs are the flexible all-rounder with a mature ecosystem; TPUs are the specialist that&amp;rsquo;s brutally efficient at large-scale matrix math — plus a rundown of who actually trains on TPUs.</description></item><item><title>Looking Again at NVIDIA's Real Logic After CES 2026</title><link>https://chengyu.eu/posts/nvidia-ces-2026-token-economics/</link><pubDate>Wed, 07 Jan 2026 12:00:00 +0000</pubDate><guid>https://chengyu.eu/posts/nvidia-ces-2026-token-economics/</guid><description>Jensen Huang&amp;rsquo;s keynote wasn&amp;rsquo;t really about FLOPS — it was about token cost, and NVIDIA quietly shifting from selling chips to selling the entire stack.</description></item></channel></rss>