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<!doctype html><html lang=en><head><title>Eric X. Liu's Personal Page</title><meta charset=utf-8><meta name=viewport content="width=device-width,initial-scale=1"><meta name=color-scheme content="light dark"><meta name=author content="Eric X. Liu"><meta name=description content="Eric X. Liu - Software & Performance Engineer at Google. Sharing insights about software engineering, performance optimization, tech industry experiences, mountain biking adventures, Jeep overlanding, and outdoor activities."><meta name=keywords content="software engineer,performance engineering,Google engineer,tech blog,software development,performance optimization,Eric Liu,engineering blog,mountain biking,Jeep enthusiast,overlanding,camping,outdoor adventures"><meta name=twitter:card content="summary"><meta name=twitter:title content="404 Page not found"><meta name=twitter:description content="Eric X. Liu - Software & Performance Engineer at Google. Sharing insights about software engineering, performance optimization, tech industry experiences, mountain biking adventures, Jeep overlanding, and outdoor activities."><meta property="og:url" content="https://ericxliu.me/404.html"><meta property="og:site_name" content="Eric X. Liu's Personal Page"><meta property="og:title" content="404 Page not found"><meta property="og:description" content="Eric X. Liu - Software & Performance Engineer at Google. Sharing insights about software engineering, performance optimization, tech industry experiences, mountain biking adventures, Jeep overlanding, and outdoor activities."><meta property="og:locale" content="en"><meta property="og:type" content="website"><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=canonical href=https://ericxliu.me/404.html><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-regular-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=stylesheet href=/css/coder.min.f03d6359cf766772af14fbe07ce6aca734b321c2e15acba0bbf4e2254941c460.css integrity="sha256-8D1jWc92Z3KvFPvgfOaspzSzIcLhWsugu/TiJUlBxGA=" crossorigin=anonymous media=screen><link rel=stylesheet href=/css/coder-dark.min.a00e6364bacbc8266ad1cc81230774a1397198f8cfb7bcba29b7d6fcb54ce57f.css integrity="sha256-oA5jZLrLyCZq0cyBIwd0oTlxmPjPt7y6KbfW/LVM5X8=" crossorigin=anonymous media=screen><link rel=icon type=image/svg+xml href=/images/favicon.svg sizes=any><link rel=icon type=image/png href=/images/favicon-32x32.png sizes=32x32><link rel=icon type=image/png href=/images/favicon-16x16.png sizes=16x16><link rel=apple-touch-icon href=/images/apple-touch-icon.png><link rel=apple-touch-icon sizes=180x180 href=/images/apple-touch-icon.png><link rel=manifest href=/site.webmanifest><link rel=mask-icon href=/images/safari-pinned-tab.svg color=#5bbad5><script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-3972604619956476" crossorigin=anonymous></script><script type=application/ld+json>{"@context":"http://schema.org","@type":"Person","name":"Eric X. Liu","url":"https:\/\/ericxliu.me\/","description":"Software \u0026 Performance Engineer at Google","sameAs":["https:\/\/www.linkedin.com\/in\/eric-x-liu-46648b93\/","https:\/\/git.ericxliu.me\/eric"]}</script></head><body class="preload-transitions colorscheme-auto"><div class=float-container><a id=dark-mode-toggle class=colorscheme-toggle><i class="fa-solid fa-adjust fa-fw" aria-hidden=true></i></a></div><main class=wrapper><nav class=navigation><section class=container><a class=navigation-title href=https://ericxliu.me/>Eric X. Liu's Personal Page
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2016 -
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2025
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Eric X. Liu
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@@ -9,10 +9,10 @@ I am a Staff Software Engineer and Tech Lead Manager (TLM) at Google, based in S
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My work focuses on Platforms Performance and Customer Engineering, specifically for GPUs and TPUs. I lead teams that bridge the gap between cutting-edge AI hardware and the latest ML models (like Gemini), ensuring optimal performance and reliability at Google Cloud scale.
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Beyond the code, I maintain this “digital garden” where I document my projects and learnings. It serves as my second brain, capturing everything from technical deep dives to random musings."><meta property="og:locale" content="en"><meta property="og:type" content="article"><meta property="article:published_time" content="2025-12-19T22:46:12-08:00"><meta property="article:modified_time" content="2025-12-19T23:02:31-08:00"><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=canonical href=https://ericxliu.me/about/><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-regular-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=stylesheet href=/css/coder.min.f03d6359cf766772af14fbe07ce6aca734b321c2e15acba0bbf4e2254941c460.css integrity="sha256-8D1jWc92Z3KvFPvgfOaspzSzIcLhWsugu/TiJUlBxGA=" crossorigin=anonymous media=screen><link rel=stylesheet href=/css/coder-dark.min.a00e6364bacbc8266ad1cc81230774a1397198f8cfb7bcba29b7d6fcb54ce57f.css integrity="sha256-oA5jZLrLyCZq0cyBIwd0oTlxmPjPt7y6KbfW/LVM5X8=" crossorigin=anonymous media=screen><link rel=icon type=image/svg+xml href=/images/favicon.svg sizes=any><link rel=icon type=image/png href=/images/favicon-32x32.png sizes=32x32><link rel=icon type=image/png href=/images/favicon-16x16.png sizes=16x16><link rel=apple-touch-icon href=/images/apple-touch-icon.png><link rel=apple-touch-icon sizes=180x180 href=/images/apple-touch-icon.png><link rel=manifest href=/site.webmanifest><link rel=mask-icon href=/images/safari-pinned-tab.svg color=#5bbad5><script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-3972604619956476" crossorigin=anonymous></script><script type=application/ld+json>{"@context":"http://schema.org","@type":"Person","name":"Eric X. Liu","url":"https:\/\/ericxliu.me\/","description":"Software \u0026 Performance Engineer at Google","sameAs":["https:\/\/www.linkedin.com\/in\/eric-x-liu-46648b93\/","https:\/\/git.ericxliu.me\/eric"]}</script><script type=application/ld+json>{"@context":"http://schema.org","@type":"BlogPosting","headline":"About","genre":"Blog","wordcount":"144","url":"https:\/\/ericxliu.me\/about\/","datePublished":"2025-12-19T22:46:12-08:00","dateModified":"2025-12-19T23:02:31-08:00","description":"\u003cp\u003eHi, I\u0026rsquo;m \u003cstrong\u003eEric Liu\u003c\/strong\u003e.\u003c\/p\u003e\n\u003cp\u003eI am a \u003cstrong\u003eStaff Software Engineer and Tech Lead Manager (TLM)\u003c\/strong\u003e at \u003cstrong\u003eGoogle\u003c\/strong\u003e, based in Sunnyvale, CA.\u003c\/p\u003e\n\u003cp\u003eMy work focuses on \u003cstrong\u003ePlatforms Performance and Customer Engineering\u003c\/strong\u003e, specifically for \u003cstrong\u003eGPUs and TPUs\u003c\/strong\u003e. I lead teams that bridge the gap between cutting-edge AI hardware and the latest ML models (like Gemini), ensuring optimal performance and reliability at Google Cloud scale.\u003c\/p\u003e\n\u003cp\u003eBeyond the code, I maintain this \u0026ldquo;digital garden\u0026rdquo; where I document my projects and learnings. It serves as my second brain, capturing everything from technical deep dives to random musings.\u003c\/p\u003e","author":{"@type":"Person","name":"Eric X. Liu"}}</script></head><body class="preload-transitions colorscheme-auto"><div class=float-container><a id=dark-mode-toggle class=colorscheme-toggle><i class="fa-solid fa-adjust fa-fw" aria-hidden=true></i></a></div><main class=wrapper><nav class=navigation><section class=container><a class=navigation-title href=https://ericxliu.me/>Eric X. Liu's Personal Page
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container page"><article><header><h1 class=title><a class=title-link href=https://ericxliu.me/about/>About</a></h1></header><p>Hi, I’m <strong>Eric Liu</strong>.</p><p>I am a <strong>Staff Software Engineer and Tech Lead Manager (TLM)</strong> at <strong>Google</strong>, based in Sunnyvale, CA.</p><p>My work focuses on <strong>Platforms Performance and Customer Engineering</strong>, specifically for <strong>GPUs and TPUs</strong>. I lead teams that bridge the gap between cutting-edge AI hardware and the latest ML models (like Gemini), ensuring optimal performance and reliability at Google Cloud scale.</p><p>Beyond the code, I maintain this “digital garden” where I document my projects and learnings. It serves as my second brain, capturing everything from technical deep dives to random musings.</p><h3 id=personal-interests>Personal Interests
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/about/>About</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container page"><article><header><h1 class=title><a class=title-link href=https://ericxliu.me/about/>About</a></h1></header><p>Hi, I’m <strong>Eric Liu</strong>.</p><p>I am a <strong>Staff Software Engineer and Tech Lead Manager (TLM)</strong> at <strong>Google</strong>, based in Sunnyvale, CA.</p><p>My work focuses on <strong>Platforms Performance and Customer Engineering</strong>, specifically for <strong>GPUs and TPUs</strong>. I lead teams that bridge the gap between cutting-edge AI hardware and the latest ML models (like Gemini), ensuring optimal performance and reliability at Google Cloud scale.</p><p>Beyond the code, I maintain this “digital garden” where I document my projects and learnings. It serves as my second brain, capturing everything from technical deep dives to random musings.</p><h3 id=personal-interests>Personal Interests
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<span class=sr-only>Link to heading</span></a></h3><p>I’m a tinkerer at heart, whether digital or physical:</p><ul><li><strong>Homelab</strong>: Kubernetes, Proxmox, and self-hosted services. I love over-engineering my home network.</li><li><strong>DIY & Jeep</strong>: Maintaining and modifying my Jeep, and general DIY projects.</li><li><strong>Cooking</strong>: experimenting with new recipes and techniques.</li></ul><p>Welcome to my corner of the internet.</p></article></section><link rel=stylesheet href=https://cdn.jsdelivr.net/npm/katex@0.16.4/dist/katex.min.css integrity=sha384-vKruj+a13U8yHIkAyGgK1J3ArTLzrFGBbBc0tDp4ad/EyewESeXE/Iv67Aj8gKZ0 crossorigin=anonymous><script defer src=https://cdn.jsdelivr.net/npm/katex@0.16.4/dist/katex.min.js integrity=sha384-PwRUT/YqbnEjkZO0zZxNqcxACrXe+j766U2amXcgMg5457rve2Y7I6ZJSm2A0mS4 crossorigin=anonymous></script><script defer src=https://cdn.jsdelivr.net/npm/katex@0.16.4/dist/contrib/auto-render.min.js integrity=sha384-+VBxd3r6XgURycqtZ117nYw44OOcIax56Z4dCRWbxyPt0Koah1uHoK0o4+/RRE05 crossorigin=anonymous onload='renderMathInElement(document.body,{delimiters:[{left:"$$",right:"$$",display:!0},{left:"$",right:"$",display:!1},{left:"\\(",right:"\\)",display:!1},{left:"\\[",right:"\\]",display:!0}]})'></script></div><footer class=footer><section class=container>©
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2016 -
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2025
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Eric X. Liu
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<!doctype html><html lang=en><head><title>Categories · Eric X. Liu's Personal Page</title><meta charset=utf-8><meta name=viewport content="width=device-width,initial-scale=1"><meta name=color-scheme content="light dark"><meta name=author content="Eric X. Liu"><meta name=description content="Eric X. Liu - Software & Performance Engineer at Google. Sharing insights about software engineering, performance optimization, tech industry experiences, mountain biking adventures, Jeep overlanding, and outdoor activities."><meta name=keywords content="software engineer,performance engineering,Google engineer,tech blog,software development,performance optimization,Eric Liu,engineering blog,mountain biking,Jeep enthusiast,overlanding,camping,outdoor adventures"><meta name=twitter:card content="summary"><meta name=twitter:title content="Categories"><meta name=twitter:description content="Eric X. Liu - Software & Performance Engineer at Google. Sharing insights about software engineering, performance optimization, tech industry experiences, mountain biking adventures, Jeep overlanding, and outdoor activities."><meta property="og:url" content="https://ericxliu.me/categories/"><meta property="og:site_name" content="Eric X. Liu's Personal Page"><meta property="og:title" content="Categories"><meta property="og:description" content="Eric X. Liu - Software & Performance Engineer at Google. Sharing insights about software engineering, performance optimization, tech industry experiences, mountain biking adventures, Jeep overlanding, and outdoor activities."><meta property="og:locale" content="en"><meta property="og:type" content="website"><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=canonical href=https://ericxliu.me/categories/><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-regular-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=stylesheet href=/css/coder.min.f03d6359cf766772af14fbe07ce6aca734b321c2e15acba0bbf4e2254941c460.css integrity="sha256-8D1jWc92Z3KvFPvgfOaspzSzIcLhWsugu/TiJUlBxGA=" crossorigin=anonymous media=screen><link rel=stylesheet href=/css/coder-dark.min.a00e6364bacbc8266ad1cc81230774a1397198f8cfb7bcba29b7d6fcb54ce57f.css integrity="sha256-oA5jZLrLyCZq0cyBIwd0oTlxmPjPt7y6KbfW/LVM5X8=" crossorigin=anonymous media=screen><link rel=icon type=image/svg+xml href=/images/favicon.svg sizes=any><link rel=icon type=image/png href=/images/favicon-32x32.png sizes=32x32><link rel=icon type=image/png href=/images/favicon-16x16.png sizes=16x16><link rel=apple-touch-icon href=/images/apple-touch-icon.png><link rel=apple-touch-icon sizes=180x180 href=/images/apple-touch-icon.png><link rel=manifest href=/site.webmanifest><link rel=mask-icon href=/images/safari-pinned-tab.svg color=#5bbad5><link rel=alternate type=application/rss+xml href=/categories/index.xml title="Eric X. Liu's Personal Page"><script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-3972604619956476" crossorigin=anonymous></script><script type=application/ld+json>{"@context":"http://schema.org","@type":"Person","name":"Eric X. Liu","url":"https:\/\/ericxliu.me\/","description":"Software \u0026 Performance Engineer at Google","sameAs":["https:\/\/www.linkedin.com\/in\/eric-x-liu-46648b93\/","https:\/\/git.ericxliu.me\/eric"]}</script></head><body class="preload-transitions colorscheme-auto"><div class=float-container><a id=dark-mode-toggle class=colorscheme-toggle><i class="fa-solid fa-adjust fa-fw" aria-hidden=true></i></a></div><main class=wrapper><nav class=navigation><section class=container><a class=navigation-title href=https://ericxliu.me/>Eric X. Liu's Personal Page
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2016 -
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2025
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Eric X. Liu
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<!doctype html><html lang=en><head><title>Eric X. Liu's Personal Page</title><meta charset=utf-8><meta name=viewport content="width=device-width,initial-scale=1"><meta name=color-scheme content="light dark"><meta name=author content="Eric X. Liu"><meta name=description content="Eric X. Liu - Software & Performance Engineer at Google. Sharing insights about software engineering, performance optimization, tech industry experiences, mountain biking adventures, Jeep overlanding, and outdoor activities."><meta name=keywords content="software engineer,performance engineering,Google engineer,tech blog,software development,performance optimization,Eric Liu,engineering blog,mountain biking,Jeep enthusiast,overlanding,camping,outdoor adventures"><meta name=twitter:card content="summary"><meta name=twitter:title content="Eric X. Liu's Personal Page"><meta name=twitter:description content="Eric X. Liu - Software & Performance Engineer at Google. Sharing insights about software engineering, performance optimization, tech industry experiences, mountain biking adventures, Jeep overlanding, and outdoor activities."><meta property="og:url" content="https://ericxliu.me/"><meta property="og:site_name" content="Eric X. Liu's Personal Page"><meta property="og:title" content="Eric X. Liu's Personal Page"><meta property="og:description" content="Eric X. Liu - Software & Performance Engineer at Google. Sharing insights about software engineering, performance optimization, tech industry experiences, mountain biking adventures, Jeep overlanding, and outdoor activities."><meta property="og:locale" content="en"><meta property="og:type" content="website"><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=canonical href=https://ericxliu.me/><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-regular-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=stylesheet href=/css/coder.min.f03d6359cf766772af14fbe07ce6aca734b321c2e15acba0bbf4e2254941c460.css integrity="sha256-8D1jWc92Z3KvFPvgfOaspzSzIcLhWsugu/TiJUlBxGA=" crossorigin=anonymous media=screen><link rel=stylesheet href=/css/coder-dark.min.a00e6364bacbc8266ad1cc81230774a1397198f8cfb7bcba29b7d6fcb54ce57f.css integrity="sha256-oA5jZLrLyCZq0cyBIwd0oTlxmPjPt7y6KbfW/LVM5X8=" crossorigin=anonymous media=screen><link rel=icon type=image/svg+xml href=/images/favicon.svg sizes=any><link rel=icon type=image/png href=/images/favicon-32x32.png sizes=32x32><link rel=icon type=image/png href=/images/favicon-16x16.png sizes=16x16><link rel=apple-touch-icon href=/images/apple-touch-icon.png><link rel=apple-touch-icon sizes=180x180 href=/images/apple-touch-icon.png><link rel=manifest href=/site.webmanifest><link rel=mask-icon href=/images/safari-pinned-tab.svg color=#5bbad5><link rel=alternate type=application/rss+xml href=/index.xml title="Eric X. Liu's Personal Page"><meta name=generator content="Hugo 0.153.0"><script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-3972604619956476" crossorigin=anonymous></script><script type=application/ld+json>{"@context":"http://schema.org","@type":"Person","name":"Eric X. Liu","url":"https:\/\/ericxliu.me\/","description":"Software \u0026 Performance Engineer at Google","sameAs":["https:\/\/www.linkedin.com\/in\/eric-x-liu-46648b93\/","https:\/\/git.ericxliu.me\/eric"]}</script></head><body class="preload-transitions colorscheme-auto"><div class=float-container><a id=dark-mode-toggle class=colorscheme-toggle><i class="fa-solid fa-adjust fa-fw" aria-hidden=true></i></a></div><main class=wrapper><nav class=navigation><section class=container><a class=navigation-title href=https://ericxliu.me/>Eric X. Liu's Personal Page
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container centered"><div class=about><div class=avatar><img src=/images/gravatar.png alt=avatar width=200 height=200></div><h1>Eric X. Liu</h1><h2>Software & Performance Engineer @Google</h2><ul><li><a href=https://git.ericxliu.me/eric aria-label=Git><i class="fa-brands fa-git fa-2x" aria-hidden=true></i></a></li><li><a href=https://www.linkedin.com/in/eric-x-liu-46648b93/ aria-label=linkedin><i class="fa-brands fa-linkedin fa-2x" aria-hidden=true></i></a></li><li><a href aria-label="Personal email"><i class="fa fa-envelope fa-2x" aria-hidden=true></i></a></li></ul></div></section></div><footer class=footer><section class=container>©
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/about/>About</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container centered"><div class=about><div class=avatar><img src=/images/gravatar.png alt=avatar width=200 height=200></div><h1>Eric X. Liu</h1><h2>Software & Performance Engineer @Google</h2><ul><li><a href=https://git.ericxliu.me/eric aria-label=Git><i class="fa-brands fa-git fa-2x" aria-hidden=true></i></a></li><li><a href=https://www.linkedin.com/in/eric-x-liu-46648b93/ aria-label=linkedin><i class="fa-brands fa-linkedin fa-2x" aria-hidden=true></i></a></li><li><a href aria-label="Personal email"><i class="fa fa-envelope fa-2x" aria-hidden=true></i></a></li></ul></div></section></div><footer class=footer><section class=container>©
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2016 -
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2025
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@@ -10,7 +10,7 @@ After running 66 inference tests across seven different language models ranging
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After running 66 inference tests across seven different language models ranging from 0.5B to 5.4B parameters, I discovered something counterintuitive: the device’s computational muscle sits largely idle during single-stream LLM inference. The bottleneck isn’t computation—it’s memory bandwidth. This isn’t just a quirk of one device; it’s a fundamental characteristic of single-user, autoregressive token generation on edge hardware—a reality that shapes how we should approach local LLM deployment."><meta property="og:url" content="https://ericxliu.me/posts/benchmarking-llms-on-jetson-orin-nano/"><meta property="og:site_name" content="Eric X. Liu's Personal Page"><meta property="og:title" content="Why Your Jetson Orin Nano's 40 TOPS Goes Unused (And What That Means for Edge AI)"><meta property="og:description" content="Introduction Link to heading NVIDIA’s Jetson Orin Nano promises impressive specs: 1024 CUDA cores, 32 Tensor Cores, and 40 TOPS of INT8 compute performance packed into a compact, power-efficient edge device. On paper, it looks like a capable platform for running Large Language Models locally. But there’s a catch—one that reveals a fundamental tension in modern edge AI hardware design.
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After running 66 inference tests across seven different language models ranging from 0.5B to 5.4B parameters, I discovered something counterintuitive: the device’s computational muscle sits largely idle during single-stream LLM inference. The bottleneck isn’t computation—it’s memory bandwidth. This isn’t just a quirk of one device; it’s a fundamental characteristic of single-user, autoregressive token generation on edge hardware—a reality that shapes how we should approach local LLM deployment."><meta property="og:locale" content="en"><meta property="og:type" content="article"><meta property="article:section" content="posts"><meta property="article:published_time" content="2025-10-04T00:00:00+00:00"><meta property="article:modified_time" content="2025-10-04T20:41:50+00:00"><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=canonical href=https://ericxliu.me/posts/benchmarking-llms-on-jetson-orin-nano/><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-regular-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=stylesheet href=/css/coder.min.f03d6359cf766772af14fbe07ce6aca734b321c2e15acba0bbf4e2254941c460.css integrity="sha256-8D1jWc92Z3KvFPvgfOaspzSzIcLhWsugu/TiJUlBxGA=" crossorigin=anonymous media=screen><link rel=stylesheet href=/css/coder-dark.min.a00e6364bacbc8266ad1cc81230774a1397198f8cfb7bcba29b7d6fcb54ce57f.css integrity="sha256-oA5jZLrLyCZq0cyBIwd0oTlxmPjPt7y6KbfW/LVM5X8=" crossorigin=anonymous media=screen><link rel=icon type=image/svg+xml href=/images/favicon.svg sizes=any><link rel=icon type=image/png href=/images/favicon-32x32.png sizes=32x32><link rel=icon type=image/png href=/images/favicon-16x16.png sizes=16x16><link rel=apple-touch-icon href=/images/apple-touch-icon.png><link rel=apple-touch-icon sizes=180x180 href=/images/apple-touch-icon.png><link rel=manifest href=/site.webmanifest><link rel=mask-icon href=/images/safari-pinned-tab.svg color=#5bbad5><script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-3972604619956476" crossorigin=anonymous></script><script type=application/ld+json>{"@context":"http://schema.org","@type":"Person","name":"Eric X. Liu","url":"https:\/\/ericxliu.me\/","description":"Software \u0026 Performance Engineer at Google","sameAs":["https:\/\/www.linkedin.com\/in\/eric-x-liu-46648b93\/","https:\/\/git.ericxliu.me\/eric"]}</script><script type=application/ld+json>{"@context":"http://schema.org","@type":"BlogPosting","headline":"Why Your Jetson Orin Nano\u0027s 40 TOPS Goes Unused (And What That Means for Edge AI)","genre":"Blog","wordcount":"1866","url":"https:\/\/ericxliu.me\/posts\/benchmarking-llms-on-jetson-orin-nano\/","datePublished":"2025-10-04T00:00:00\u002b00:00","dateModified":"2025-10-04T20:41:50\u002b00:00","description":"\u003ch2 id=\u0022introduction\u0022\u003e\n Introduction\n \u003ca class=\u0022heading-link\u0022 href=\u0022#introduction\u0022\u003e\n \u003ci class=\u0022fa-solid fa-link\u0022 aria-hidden=\u0022true\u0022 title=\u0022Link to heading\u0022\u003e\u003c\/i\u003e\n \u003cspan class=\u0022sr-only\u0022\u003eLink to heading\u003c\/span\u003e\n \u003c\/a\u003e\n\u003c\/h2\u003e\n\u003cp\u003eNVIDIA\u0026rsquo;s Jetson Orin Nano promises impressive specs: 1024 CUDA cores, 32 Tensor Cores, and 40 TOPS of INT8 compute performance packed into a compact, power-efficient edge device. On paper, it looks like a capable platform for running Large Language Models locally. But there\u0026rsquo;s a catch—one that reveals a fundamental tension in modern edge AI hardware design.\u003c\/p\u003e\n\u003cp\u003eAfter running 66 inference tests across seven different language models ranging from 0.5B to 5.4B parameters, I discovered something counterintuitive: the device\u0026rsquo;s computational muscle sits largely idle during single-stream LLM inference. The bottleneck isn\u0026rsquo;t computation—it\u0026rsquo;s memory bandwidth. This isn\u0026rsquo;t just a quirk of one device; it\u0026rsquo;s a fundamental characteristic of single-user, autoregressive token generation on edge hardware—a reality that shapes how we should approach local LLM deployment.\u003c\/p\u003e","author":{"@type":"Person","name":"Eric X. Liu"}}</script></head><body class="preload-transitions colorscheme-auto"><div class=float-container><a id=dark-mode-toggle class=colorscheme-toggle><i class="fa-solid fa-adjust fa-fw" aria-hidden=true></i></a></div><main class=wrapper><nav class=navigation><section class=container><a class=navigation-title href=https://ericxliu.me/>Eric X. Liu's Personal Page
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container post"><article><header><div class=post-title><h1 class=title><a class=title-link href=https://ericxliu.me/posts/benchmarking-llms-on-jetson-orin-nano/>Why Your Jetson Orin Nano's 40 TOPS Goes Unused (And What That Means for Edge AI)</a></h1></div><div class=post-meta><div class=date><span class=posted-on><i class="fa-solid fa-calendar" aria-hidden=true></i>
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/about/>About</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container post"><article><header><div class=post-title><h1 class=title><a class=title-link href=https://ericxliu.me/posts/benchmarking-llms-on-jetson-orin-nano/>Why Your Jetson Orin Nano's 40 TOPS Goes Unused (And What That Means for Edge AI)</a></h1></div><div class=post-meta><div class=date><span class=posted-on><i class="fa-solid fa-calendar" aria-hidden=true></i>
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<time datetime=2025-10-04T00:00:00Z>October 4, 2025
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</time></span><span class=reading-time><i class="fa-solid fa-clock" aria-hidden=true></i>
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9-minute read</span></div></div></header><div class=post-content><h2 id=introduction>Introduction
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@@ -62,4 +62,4 @@ After running 66 inference tests across seven different language models ranging
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2016 -
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2025
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Eric X. Liu
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<a href="https://git.ericxliu.me/eric/ericxliu-me/commit/3f9f80d">[3f9f80d]</a></section></footer></main><script src=/js/coder.min.6ae284be93d2d19dad1f02b0039508d9aab3180a12a06dcc71b0b0ef7825a317.js integrity="sha256-auKEvpPS0Z2tHwKwA5UI2aqzGAoSoG3McbCw73gloxc="></script><script defer src=https://static.cloudflareinsights.com/beacon.min.js data-cf-beacon='{"token": "987638e636ce4dbb932d038af74c17d1"}'></script></body></html>
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@@ -10,7 +10,7 @@ The Breville Barista Pro has two distinct, automated maintenance procedures: the
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Understanding the Two Primary Maintenance Cycles Link to heading The Breville Barista Pro has two distinct, automated maintenance procedures: the Cleaning (Flush) Cycle and the Descale Cycle. It is important to understand that these are not interchangeable, as they address different types of buildup within the machine."><meta property="og:url" content="https://ericxliu.me/posts/breville-barista-pro-maintenance/"><meta property="og:site_name" content="Eric X. Liu's Personal Page"><meta property="og:title" content="Breville Barista Pro Maintenance"><meta property="og:description" content="Proper maintenance is critical for the longevity and performance of a Breville Barista Pro espresso machine. Consistent cleaning not only ensures the machine functions correctly but also directly impacts the quality of the espresso produced. This guide provides a detailed, technical breakdown of the essential maintenance routines, from automated cycles to daily upkeep.
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Understanding the Two Primary Maintenance Cycles Link to heading The Breville Barista Pro has two distinct, automated maintenance procedures: the Cleaning (Flush) Cycle and the Descale Cycle. It is important to understand that these are not interchangeable, as they address different types of buildup within the machine."><meta property="og:locale" content="en"><meta property="og:type" content="article"><meta property="article:section" content="posts"><meta property="article:published_time" content="2025-08-16T00:00:00+00:00"><meta property="article:modified_time" content="2025-08-20T06:04:36+00:00"><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=canonical href=https://ericxliu.me/posts/breville-barista-pro-maintenance/><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-regular-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=stylesheet href=/css/coder.min.f03d6359cf766772af14fbe07ce6aca734b321c2e15acba0bbf4e2254941c460.css integrity="sha256-8D1jWc92Z3KvFPvgfOaspzSzIcLhWsugu/TiJUlBxGA=" crossorigin=anonymous media=screen><link rel=stylesheet href=/css/coder-dark.min.a00e6364bacbc8266ad1cc81230774a1397198f8cfb7bcba29b7d6fcb54ce57f.css integrity="sha256-oA5jZLrLyCZq0cyBIwd0oTlxmPjPt7y6KbfW/LVM5X8=" crossorigin=anonymous media=screen><link rel=icon type=image/svg+xml href=/images/favicon.svg sizes=any><link rel=icon type=image/png href=/images/favicon-32x32.png sizes=32x32><link rel=icon type=image/png href=/images/favicon-16x16.png sizes=16x16><link rel=apple-touch-icon href=/images/apple-touch-icon.png><link rel=apple-touch-icon sizes=180x180 href=/images/apple-touch-icon.png><link rel=manifest href=/site.webmanifest><link rel=mask-icon href=/images/safari-pinned-tab.svg color=#5bbad5><script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-3972604619956476" crossorigin=anonymous></script><script type=application/ld+json>{"@context":"http://schema.org","@type":"Person","name":"Eric X. Liu","url":"https:\/\/ericxliu.me\/","description":"Software \u0026 Performance Engineer at Google","sameAs":["https:\/\/www.linkedin.com\/in\/eric-x-liu-46648b93\/","https:\/\/git.ericxliu.me\/eric"]}</script><script type=application/ld+json>{"@context":"http://schema.org","@type":"BlogPosting","headline":"Breville Barista Pro Maintenance","genre":"Blog","wordcount":"920","url":"https:\/\/ericxliu.me\/posts\/breville-barista-pro-maintenance\/","datePublished":"2025-08-16T00:00:00\u002b00:00","dateModified":"2025-08-20T06:04:36\u002b00:00","description":"\u003cp\u003eProper maintenance is critical for the longevity and performance of a Breville Barista Pro espresso machine. Consistent cleaning not only ensures the machine functions correctly but also directly impacts the quality of the espresso produced. This guide provides a detailed, technical breakdown of the essential maintenance routines, from automated cycles to daily upkeep.\u003c\/p\u003e\n\u003ch4 id=\u0022understanding-the-two-primary-maintenance-cycles\u0022\u003e\n \u003cstrong\u003eUnderstanding the Two Primary Maintenance Cycles\u003c\/strong\u003e\n \u003ca class=\u0022heading-link\u0022 href=\u0022#understanding-the-two-primary-maintenance-cycles\u0022\u003e\n \u003ci class=\u0022fa-solid fa-link\u0022 aria-hidden=\u0022true\u0022 title=\u0022Link to heading\u0022\u003e\u003c\/i\u003e\n \u003cspan class=\u0022sr-only\u0022\u003eLink to heading\u003c\/span\u003e\n \u003c\/a\u003e\n\u003c\/h4\u003e\n\u003cp\u003eThe Breville Barista Pro has two distinct, automated maintenance procedures: the \u003cstrong\u003eCleaning (Flush) Cycle\u003c\/strong\u003e and the \u003cstrong\u003eDescale Cycle\u003c\/strong\u003e. It is important to understand that these are not interchangeable, as they address different types of buildup within the machine.\u003c\/p\u003e","author":{"@type":"Person","name":"Eric X. Liu"}}</script></head><body class="preload-transitions colorscheme-auto"><div class=float-container><a id=dark-mode-toggle class=colorscheme-toggle><i class="fa-solid fa-adjust fa-fw" aria-hidden=true></i></a></div><main class=wrapper><nav class=navigation><section class=container><a class=navigation-title href=https://ericxliu.me/>Eric X. Liu's Personal Page
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container post"><article><header><div class=post-title><h1 class=title><a class=title-link href=https://ericxliu.me/posts/breville-barista-pro-maintenance/>Breville Barista Pro Maintenance</a></h1></div><div class=post-meta><div class=date><span class=posted-on><i class="fa-solid fa-calendar" aria-hidden=true></i>
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/about/>About</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container post"><article><header><div class=post-title><h1 class=title><a class=title-link href=https://ericxliu.me/posts/breville-barista-pro-maintenance/>Breville Barista Pro Maintenance</a></h1></div><div class=post-meta><div class=date><span class=posted-on><i class="fa-solid fa-calendar" aria-hidden=true></i>
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<time datetime=2025-08-16T00:00:00Z>August 16, 2025
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</time></span><span class=reading-time><i class="fa-solid fa-clock" aria-hidden=true></i>
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5-minute read</span></div></div></header><div class=post-content><p>Proper maintenance is critical for the longevity and performance of a Breville Barista Pro espresso machine. Consistent cleaning not only ensures the machine functions correctly but also directly impacts the quality of the espresso produced. This guide provides a detailed, technical breakdown of the essential maintenance routines, from automated cycles to daily upkeep.</p><h4 id=understanding-the-two-primary-maintenance-cycles><strong>Understanding the Two Primary Maintenance Cycles</strong>
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@@ -25,4 +25,4 @@ Understanding the Two Primary Maintenance Cycles Link to heading The Breville Ba
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2025
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Eric X. Liu
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@@ -3,7 +3,7 @@ Our overarching philosophy is simple: isolate and change only one variable at a
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Our overarching philosophy is simple: isolate and change only one variable at a time. While numbers are crucial, your palate is the ultimate judge. Dose, ratio, and time are interconnected, but your grind size is your most powerful lever."><meta property="og:url" content="https://ericxliu.me/posts/espresso-theory-application-a-guide-for-the-breville-barista-pro/"><meta property="og:site_name" content="Eric X. Liu's Personal Page"><meta property="og:title" content="Mastering Your Breville Barista Pro: The Ultimate Guide to Dialing In Espresso"><meta property="og:description" content="Are you ready to transform your home espresso game from good to genuinely great? The Breville Barista Pro is a fantastic machine, but unlocking its full potential requires understanding a few key principles. This guide will walk you through the systematic process of dialing in your espresso, ensuring every shot is delicious and repeatable.
|
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Our overarching philosophy is simple: isolate and change only one variable at a time. While numbers are crucial, your palate is the ultimate judge. Dose, ratio, and time are interconnected, but your grind size is your most powerful lever."><meta property="og:locale" content="en"><meta property="og:type" content="article"><meta property="article:section" content="posts"><meta property="article:published_time" content="2025-05-01T00:00:00+00:00"><meta property="article:modified_time" content="2025-08-03T04:20:20+00:00"><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=canonical href=https://ericxliu.me/posts/espresso-theory-application-a-guide-for-the-breville-barista-pro/><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-regular-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=stylesheet href=/css/coder.min.f03d6359cf766772af14fbe07ce6aca734b321c2e15acba0bbf4e2254941c460.css integrity="sha256-8D1jWc92Z3KvFPvgfOaspzSzIcLhWsugu/TiJUlBxGA=" crossorigin=anonymous media=screen><link rel=stylesheet href=/css/coder-dark.min.a00e6364bacbc8266ad1cc81230774a1397198f8cfb7bcba29b7d6fcb54ce57f.css integrity="sha256-oA5jZLrLyCZq0cyBIwd0oTlxmPjPt7y6KbfW/LVM5X8=" crossorigin=anonymous media=screen><link rel=icon type=image/svg+xml href=/images/favicon.svg sizes=any><link rel=icon type=image/png href=/images/favicon-32x32.png sizes=32x32><link rel=icon type=image/png href=/images/favicon-16x16.png sizes=16x16><link rel=apple-touch-icon href=/images/apple-touch-icon.png><link rel=apple-touch-icon sizes=180x180 href=/images/apple-touch-icon.png><link rel=manifest href=/site.webmanifest><link rel=mask-icon href=/images/safari-pinned-tab.svg color=#5bbad5><script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-3972604619956476" crossorigin=anonymous></script><script type=application/ld+json>{"@context":"http://schema.org","@type":"Person","name":"Eric X. Liu","url":"https:\/\/ericxliu.me\/","description":"Software \u0026 Performance Engineer at Google","sameAs":["https:\/\/www.linkedin.com\/in\/eric-x-liu-46648b93\/","https:\/\/git.ericxliu.me\/eric"]}</script><script type=application/ld+json>{"@context":"http://schema.org","@type":"BlogPosting","headline":"Mastering Your Breville Barista Pro: The Ultimate Guide to Dialing In Espresso","genre":"Blog","wordcount":"1125","url":"https:\/\/ericxliu.me\/posts\/espresso-theory-application-a-guide-for-the-breville-barista-pro\/","datePublished":"2025-05-01T00:00:00\u002b00:00","dateModified":"2025-08-03T04:20:20\u002b00:00","description":"\u003cp\u003eAre you ready to transform your home espresso game from good to genuinely great? The Breville Barista Pro is a fantastic machine, but unlocking its full potential requires understanding a few key principles. This guide will walk you through the systematic process of dialing in your espresso, ensuring every shot is delicious and repeatable.\u003c\/p\u003e\n\u003cp\u003eOur overarching philosophy is simple: \u003cstrong\u003eisolate and change only one variable at a time.\u003c\/strong\u003e While numbers are crucial, your palate is the ultimate judge. Dose, ratio, and time are interconnected, but your \u003cstrong\u003egrind size\u003c\/strong\u003e is your most powerful lever.\u003c\/p\u003e","author":{"@type":"Person","name":"Eric X. Liu"}}</script></head><body class="preload-transitions colorscheme-auto"><div class=float-container><a id=dark-mode-toggle class=colorscheme-toggle><i class="fa-solid fa-adjust fa-fw" aria-hidden=true></i></a></div><main class=wrapper><nav class=navigation><section class=container><a class=navigation-title href=https://ericxliu.me/>Eric X. Liu's Personal Page
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container post"><article><header><div class=post-title><h1 class=title><a class=title-link href=https://ericxliu.me/posts/espresso-theory-application-a-guide-for-the-breville-barista-pro/>Mastering Your Breville Barista Pro: The Ultimate Guide to Dialing In Espresso</a></h1></div><div class=post-meta><div class=date><span class=posted-on><i class="fa-solid fa-calendar" aria-hidden=true></i>
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/about/>About</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container post"><article><header><div class=post-title><h1 class=title><a class=title-link href=https://ericxliu.me/posts/espresso-theory-application-a-guide-for-the-breville-barista-pro/>Mastering Your Breville Barista Pro: The Ultimate Guide to Dialing In Espresso</a></h1></div><div class=post-meta><div class=date><span class=posted-on><i class="fa-solid fa-calendar" aria-hidden=true></i>
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<time datetime=2025-05-01T00:00:00Z>May 1, 2025
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</time></span><span class=reading-time><i class="fa-solid fa-clock" aria-hidden=true></i>
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6-minute read</span></div></div></header><div class=post-content><p>Are you ready to transform your home espresso game from good to genuinely great? The Breville Barista Pro is a fantastic machine, but unlocking its full potential requires understanding a few key principles. This guide will walk you through the systematic process of dialing in your espresso, ensuring every shot is delicious and repeatable.</p><p>Our overarching philosophy is simple: <strong>isolate and change only one variable at a time.</strong> While numbers are crucial, your palate is the ultimate judge. Dose, ratio, and time are interconnected, but your <strong>grind size</strong> is your most powerful lever.</p><p>Let’s dive in!</p><hr><h3 id=part-1-the-foundation--dose-the-weight-of-dry-coffee><strong>Part 1: The Foundation — Dose (The Weight of Dry Coffee)</strong>
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@@ -20,4 +20,4 @@ Our overarching philosophy is simple: isolate and change only one variable at a
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2016 -
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2025
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Eric X. Liu
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@@ -3,7 +3,7 @@ The answer lies in creating a universal language—a bridge between the continuo
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The answer lies in creating a universal language—a bridge between the continuous, messy world of pixels and audio waves and the discrete, structured world of language tokens. One of the most elegant and powerful tools for building this bridge is Residual Vector Quantization (RVQ)."><meta property="og:url" content="https://ericxliu.me/posts/how-rvq-teaches-llms-to-see-and-hear/"><meta property="og:site_name" content="Eric X. Liu's Personal Page"><meta property="og:title" content="Beyond Words: How RVQ Teaches LLMs to See and Hear"><meta property="og:description" content="Large Language Models (LLMs) are masters of text, but the world is not made of text alone. It’s a symphony of sights, sounds, and experiences. The ultimate goal for AI is to understand this rich, multi-modal world as we do. But how do you teach a model that thinks in words to understand a picture of a sunset or the melody of a song?
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The answer lies in creating a universal language—a bridge between the continuous, messy world of pixels and audio waves and the discrete, structured world of language tokens. One of the most elegant and powerful tools for building this bridge is Residual Vector Quantization (RVQ)."><meta property="og:locale" content="en"><meta property="og:type" content="article"><meta property="article:section" content="posts"><meta property="article:published_time" content="2025-08-07T00:00:00+00:00"><meta property="article:modified_time" content="2025-08-08T17:36:52+00:00"><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=canonical href=https://ericxliu.me/posts/how-rvq-teaches-llms-to-see-and-hear/><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-regular-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=stylesheet href=/css/coder.min.f03d6359cf766772af14fbe07ce6aca734b321c2e15acba0bbf4e2254941c460.css integrity="sha256-8D1jWc92Z3KvFPvgfOaspzSzIcLhWsugu/TiJUlBxGA=" crossorigin=anonymous media=screen><link rel=stylesheet href=/css/coder-dark.min.a00e6364bacbc8266ad1cc81230774a1397198f8cfb7bcba29b7d6fcb54ce57f.css integrity="sha256-oA5jZLrLyCZq0cyBIwd0oTlxmPjPt7y6KbfW/LVM5X8=" crossorigin=anonymous media=screen><link rel=icon type=image/svg+xml href=/images/favicon.svg sizes=any><link rel=icon type=image/png href=/images/favicon-32x32.png sizes=32x32><link rel=icon type=image/png href=/images/favicon-16x16.png sizes=16x16><link rel=apple-touch-icon href=/images/apple-touch-icon.png><link rel=apple-touch-icon sizes=180x180 href=/images/apple-touch-icon.png><link rel=manifest href=/site.webmanifest><link rel=mask-icon href=/images/safari-pinned-tab.svg color=#5bbad5><script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-3972604619956476" crossorigin=anonymous></script><script type=application/ld+json>{"@context":"http://schema.org","@type":"Person","name":"Eric X. Liu","url":"https:\/\/ericxliu.me\/","description":"Software \u0026 Performance Engineer at Google","sameAs":["https:\/\/www.linkedin.com\/in\/eric-x-liu-46648b93\/","https:\/\/git.ericxliu.me\/eric"]}</script><script type=application/ld+json>{"@context":"http://schema.org","@type":"BlogPosting","headline":"Beyond Words: How RVQ Teaches LLMs to See and Hear","genre":"Blog","wordcount":"1150","url":"https:\/\/ericxliu.me\/posts\/how-rvq-teaches-llms-to-see-and-hear\/","datePublished":"2025-08-07T00:00:00\u002b00:00","dateModified":"2025-08-08T17:36:52\u002b00:00","description":"\u003cp\u003eLarge Language Models (LLMs) are masters of text, but the world is not made of text alone. It’s a symphony of sights, sounds, and experiences. The ultimate goal for AI is to understand this rich, multi-modal world as we do. But how do you teach a model that thinks in words to understand a picture of a sunset or the melody of a song?\u003c\/p\u003e\n\u003cp\u003eThe answer lies in creating a universal language—a bridge between the continuous, messy world of pixels and audio waves and the discrete, structured world of language tokens. One of the most elegant and powerful tools for building this bridge is \u003cstrong\u003eResidual Vector Quantization (RVQ)\u003c\/strong\u003e.\u003c\/p\u003e","author":{"@type":"Person","name":"Eric X. Liu"}}</script></head><body class="preload-transitions colorscheme-auto"><div class=float-container><a id=dark-mode-toggle class=colorscheme-toggle><i class="fa-solid fa-adjust fa-fw" aria-hidden=true></i></a></div><main class=wrapper><nav class=navigation><section class=container><a class=navigation-title href=https://ericxliu.me/>Eric X. Liu's Personal Page
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container post"><article><header><div class=post-title><h1 class=title><a class=title-link href=https://ericxliu.me/posts/how-rvq-teaches-llms-to-see-and-hear/>Beyond Words: How RVQ Teaches LLMs to See and Hear</a></h1></div><div class=post-meta><div class=date><span class=posted-on><i class="fa-solid fa-calendar" aria-hidden=true></i>
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/about/>About</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container post"><article><header><div class=post-title><h1 class=title><a class=title-link href=https://ericxliu.me/posts/how-rvq-teaches-llms-to-see-and-hear/>Beyond Words: How RVQ Teaches LLMs to See and Hear</a></h1></div><div class=post-meta><div class=date><span class=posted-on><i class="fa-solid fa-calendar" aria-hidden=true></i>
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<time datetime=2025-08-07T00:00:00Z>August 7, 2025
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</time></span><span class=reading-time><i class="fa-solid fa-clock" aria-hidden=true></i>
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6-minute read</span></div></div></header><div class=post-content><p>Large Language Models (LLMs) are masters of text, but the world is not made of text alone. It’s a symphony of sights, sounds, and experiences. The ultimate goal for AI is to understand this rich, multi-modal world as we do. But how do you teach a model that thinks in words to understand a picture of a sunset or the melody of a song?</p><p>The answer lies in creating a universal language—a bridge between the continuous, messy world of pixels and audio waves and the discrete, structured world of language tokens. One of the most elegant and powerful tools for building this bridge is <strong>Residual Vector Quantization (RVQ)</strong>.</p><p>This article dives deep into RVQ, exploring how it turns raw data into meaningful semantic IDs and how these IDs, in turn, unlock multi-modal understanding in LLMs.</p><h4 id=what-is-residual-vector-quantization-the-art-of-smart-compression><strong>What is Residual Vector Quantization? The Art of Smart Compression</strong>
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Eric X. Liu
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<a href="https://git.ericxliu.me/eric/ericxliu-me/commit/2bb856b">[2bb856b]</a></section></footer></main><script src=/js/coder.min.6ae284be93d2d19dad1f02b0039508d9aab3180a12a06dcc71b0b0ef7825a317.js integrity="sha256-auKEvpPS0Z2tHwKwA5UI2aqzGAoSoG3McbCw73gloxc="></script><script defer src=https://static.cloudflareinsights.com/beacon.min.js data-cf-beacon='{"token": "987638e636ce4dbb932d038af74c17d1"}'></script></body></html>
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<a class=title href=/posts/the-convergence-of-fast-weights-linear-attention-and-state-space-models/>The Convergence of Fast Weights, Linear Attention, and State Space Models</a></li><li><span class=date>December 8, 2025</span>
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<a class=title href=/posts/vattention/>vAttention</a></li><li><span class=date>October 4, 2025</span>
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<a class=title href=/posts/benchmarking-llms-on-jetson-orin-nano/>Why Your Jetson Orin Nano's 40 TOPS Goes Unused (And What That Means for Edge AI)</a></li><li><span class=date>October 2, 2025</span>
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@@ -14,4 +14,4 @@
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@@ -11,7 +11,7 @@ Many routing mechanisms, especially “Top-K routing,” involve a discr
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1. Challenge: Non-Differentiability of Routing Functions Link to heading The Problem: Many routing mechanisms, especially “Top-K routing,” involve a discrete, hard selection process. A common function is KeepTopK(v, k), which selects the top k scoring elements from a vector v and sets others to $-\infty$ or $0$."><meta property="og:url" content="https://ericxliu.me/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/"><meta property="og:site_name" content="Eric X. Liu's Personal Page"><meta property="og:title" content="Mixture-of-Experts (MoE) Models Challenges & Solutions in Practice"><meta property="og:description" content="Mixture-of-Experts (MoEs) are neural network architectures that allow different parts of the model (called “experts”) to specialize in different types of inputs. A “gating network” or “router” learns to dispatch each input (or “token”) to a subset of these experts. While powerful for scaling models, MoEs introduce several practical challenges.
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1. Challenge: Non-Differentiability of Routing Functions Link to heading The Problem: Many routing mechanisms, especially “Top-K routing,” involve a discrete, hard selection process. A common function is KeepTopK(v, k), which selects the top k scoring elements from a vector v and sets others to $-\infty$ or $0$."><meta property="og:locale" content="en"><meta property="og:type" content="article"><meta property="article:section" content="posts"><meta property="article:published_time" content="2025-07-02T00:00:00+00:00"><meta property="article:modified_time" content="2025-08-03T06:02:48+00:00"><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=canonical href=https://ericxliu.me/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-regular-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=stylesheet href=/css/coder.min.f03d6359cf766772af14fbe07ce6aca734b321c2e15acba0bbf4e2254941c460.css integrity="sha256-8D1jWc92Z3KvFPvgfOaspzSzIcLhWsugu/TiJUlBxGA=" crossorigin=anonymous media=screen><link rel=stylesheet href=/css/coder-dark.min.a00e6364bacbc8266ad1cc81230774a1397198f8cfb7bcba29b7d6fcb54ce57f.css integrity="sha256-oA5jZLrLyCZq0cyBIwd0oTlxmPjPt7y6KbfW/LVM5X8=" crossorigin=anonymous media=screen><link rel=icon type=image/svg+xml href=/images/favicon.svg sizes=any><link rel=icon type=image/png href=/images/favicon-32x32.png sizes=32x32><link rel=icon type=image/png href=/images/favicon-16x16.png sizes=16x16><link rel=apple-touch-icon href=/images/apple-touch-icon.png><link rel=apple-touch-icon sizes=180x180 href=/images/apple-touch-icon.png><link rel=manifest href=/site.webmanifest><link rel=mask-icon href=/images/safari-pinned-tab.svg color=#5bbad5><script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-3972604619956476" crossorigin=anonymous></script><script type=application/ld+json>{"@context":"http://schema.org","@type":"Person","name":"Eric X. Liu","url":"https:\/\/ericxliu.me\/","description":"Software \u0026 Performance Engineer at Google","sameAs":["https:\/\/www.linkedin.com\/in\/eric-x-liu-46648b93\/","https:\/\/git.ericxliu.me\/eric"]}</script><script type=application/ld+json>{"@context":"http://schema.org","@type":"BlogPosting","headline":"Mixture-of-Experts (MoE) Models Challenges \u0026 Solutions in Practice","genre":"Blog","wordcount":"1381","url":"https:\/\/ericxliu.me\/posts\/mixture-of-experts-moe-models-challenges-solutions-in-practice\/","datePublished":"2025-07-02T00:00:00\u002b00:00","dateModified":"2025-08-03T06:02:48\u002b00:00","description":"\u003cp\u003eMixture-of-Experts (MoEs) are neural network architectures that allow different parts of the model (called \u0026ldquo;experts\u0026rdquo;) to specialize in different types of inputs. A \u0026ldquo;gating network\u0026rdquo; or \u0026ldquo;router\u0026rdquo; learns to dispatch each input (or \u0026ldquo;token\u0026rdquo;) to a subset of these experts. While powerful for scaling models, MoEs introduce several practical challenges.\u003c\/p\u003e\n\u003ch3 id=\u00221-challenge-non-differentiability-of-routing-functions\u0022\u003e\n 1. Challenge: Non-Differentiability of Routing Functions\n \u003ca class=\u0022heading-link\u0022 href=\u0022#1-challenge-non-differentiability-of-routing-functions\u0022\u003e\n \u003ci class=\u0022fa-solid fa-link\u0022 aria-hidden=\u0022true\u0022 title=\u0022Link to heading\u0022\u003e\u003c\/i\u003e\n \u003cspan class=\u0022sr-only\u0022\u003eLink to heading\u003c\/span\u003e\n \u003c\/a\u003e\n\u003c\/h3\u003e\n\u003cp\u003e\u003cstrong\u003eThe Problem:\u003c\/strong\u003e\nMany routing mechanisms, especially \u0026ldquo;Top-K routing,\u0026rdquo; involve a discrete, hard selection process. A common function is \u003ccode\u003eKeepTopK(v, k)\u003c\/code\u003e, which selects the top \u003ccode\u003ek\u003c\/code\u003e scoring elements from a vector \u003ccode\u003ev\u003c\/code\u003e and sets others to $-\\infty$ or $0$.\u003c\/p\u003e","author":{"@type":"Person","name":"Eric X. Liu"}}</script></head><body class="preload-transitions colorscheme-auto"><div class=float-container><a id=dark-mode-toggle class=colorscheme-toggle><i class="fa-solid fa-adjust fa-fw" aria-hidden=true></i></a></div><main class=wrapper><nav class=navigation><section class=container><a class=navigation-title href=https://ericxliu.me/>Eric X. Liu's Personal Page
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<time datetime=2025-07-02T00:00:00Z>July 2, 2025
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7-minute read</span></div></div></header><div class=post-content><p>Mixture-of-Experts (MoEs) are neural network architectures that allow different parts of the model (called “experts”) to specialize in different types of inputs. A “gating network” or “router” learns to dispatch each input (or “token”) to a subset of these experts. While powerful for scaling models, MoEs introduce several practical challenges.</p><h3 id=1-challenge-non-differentiability-of-routing-functions>1. Challenge: Non-Differentiability of Routing Functions
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@@ -44,4 +44,4 @@ The <strong>Top-K routing</strong> mechanism, as illustrated in the provided ima
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<a class=title href=/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/>Mixture-of-Experts (MoE) Models Challenges & Solutions in Practice</a></li><li><span class=date>June 1, 2025</span>
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@@ -11,4 +11,4 @@
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2016 -
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@@ -4,7 +4,7 @@ You may have seen diagrams like the one below, which outlines the RLHF training
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You may have seen diagrams like the one below, which outlines the RLHF training process. It can look intimidating, with a web of interconnected models, losses, and data flows."><meta property="og:url" content="https://ericxliu.me/posts/ppo-for-language-models/"><meta property="og:site_name" content="Eric X. Liu's Personal Page"><meta property="og:title" content="A Deep Dive into PPO for Language Models"><meta property="og:description" content="Large Language Models (LLMs) have demonstrated astonishing capabilities, but out-of-the-box, they are simply powerful text predictors. They don’t inherently understand what makes a response helpful, harmless, or aligned with human values. The technique that has proven most effective at bridging this gap is Reinforcement Learning from Human Feedback (RLHF), and at its heart lies a powerful algorithm: Proximal Policy Optimization (PPO).
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You may have seen diagrams like the one below, which outlines the RLHF training process. It can look intimidating, with a web of interconnected models, losses, and data flows."><meta property="og:locale" content="en"><meta property="og:type" content="article"><meta property="article:section" content="posts"><meta property="article:published_time" content="2025-08-02T00:00:00+00:00"><meta property="article:modified_time" content="2025-10-02T08:42:39+00:00"><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=canonical href=https://ericxliu.me/posts/ppo-for-language-models/><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-regular-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=stylesheet href=/css/coder.min.f03d6359cf766772af14fbe07ce6aca734b321c2e15acba0bbf4e2254941c460.css integrity="sha256-8D1jWc92Z3KvFPvgfOaspzSzIcLhWsugu/TiJUlBxGA=" crossorigin=anonymous media=screen><link rel=stylesheet href=/css/coder-dark.min.a00e6364bacbc8266ad1cc81230774a1397198f8cfb7bcba29b7d6fcb54ce57f.css integrity="sha256-oA5jZLrLyCZq0cyBIwd0oTlxmPjPt7y6KbfW/LVM5X8=" crossorigin=anonymous media=screen><link rel=icon type=image/svg+xml href=/images/favicon.svg sizes=any><link rel=icon type=image/png href=/images/favicon-32x32.png sizes=32x32><link rel=icon type=image/png href=/images/favicon-16x16.png sizes=16x16><link rel=apple-touch-icon href=/images/apple-touch-icon.png><link rel=apple-touch-icon sizes=180x180 href=/images/apple-touch-icon.png><link rel=manifest href=/site.webmanifest><link rel=mask-icon href=/images/safari-pinned-tab.svg color=#5bbad5><script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-3972604619956476" crossorigin=anonymous></script><script type=application/ld+json>{"@context":"http://schema.org","@type":"Person","name":"Eric X. Liu","url":"https:\/\/ericxliu.me\/","description":"Software \u0026 Performance Engineer at Google","sameAs":["https:\/\/www.linkedin.com\/in\/eric-x-liu-46648b93\/","https:\/\/git.ericxliu.me\/eric"]}</script><script type=application/ld+json>{"@context":"http://schema.org","@type":"BlogPosting","headline":"A Deep Dive into PPO for Language Models","genre":"Blog","wordcount":"1393","url":"https:\/\/ericxliu.me\/posts\/ppo-for-language-models\/","datePublished":"2025-08-02T00:00:00\u002b00:00","dateModified":"2025-10-02T08:42:39\u002b00:00","description":"\u003cp\u003eLarge Language Models (LLMs) have demonstrated astonishing capabilities, but out-of-the-box, they are simply powerful text predictors. They don\u0026rsquo;t inherently understand what makes a response helpful, harmless, or aligned with human values. The technique that has proven most effective at bridging this gap is Reinforcement Learning from Human Feedback (RLHF), and at its heart lies a powerful algorithm: Proximal Policy Optimization (PPO).\u003c\/p\u003e\n\u003cp\u003eYou may have seen diagrams like the one below, which outlines the RLHF training process. It can look intimidating, with a web of interconnected models, losses, and data flows.\n\u003cimg src=\u0022\/images\/ppo-for-language-models\/7713bd3ecf27442e939b9190fa08165d.png\u0022 alt=\u0022S3 File\u0022\u003e\u003c\/p\u003e","author":{"@type":"Person","name":"Eric X. Liu"}}</script></head><body class="preload-transitions colorscheme-auto"><div class=float-container><a id=dark-mode-toggle class=colorscheme-toggle><i class="fa-solid fa-adjust fa-fw" aria-hidden=true></i></a></div><main class=wrapper><nav class=navigation><section class=container><a class=navigation-title href=https://ericxliu.me/>Eric X. Liu's Personal Page
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container post"><article><header><div class=post-title><h1 class=title><a class=title-link href=https://ericxliu.me/posts/ppo-for-language-models/>A Deep Dive into PPO for Language Models</a></h1></div><div class=post-meta><div class=date><span class=posted-on><i class="fa-solid fa-calendar" aria-hidden=true></i>
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/about/>About</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container post"><article><header><div class=post-title><h1 class=title><a class=title-link href=https://ericxliu.me/posts/ppo-for-language-models/>A Deep Dive into PPO for Language Models</a></h1></div><div class=post-meta><div class=date><span class=posted-on><i class="fa-solid fa-calendar" aria-hidden=true></i>
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<time datetime=2025-08-02T00:00:00Z>August 2, 2025
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</time></span><span class=reading-time><i class="fa-solid fa-clock" aria-hidden=true></i>
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7-minute read</span></div></div></header><div class=post-content><p>Large Language Models (LLMs) have demonstrated astonishing capabilities, but out-of-the-box, they are simply powerful text predictors. They don’t inherently understand what makes a response helpful, harmless, or aligned with human values. The technique that has proven most effective at bridging this gap is Reinforcement Learning from Human Feedback (RLHF), and at its heart lies a powerful algorithm: Proximal Policy Optimization (PPO).</p><p>You may have seen diagrams like the one below, which outlines the RLHF training process. It can look intimidating, with a web of interconnected models, losses, and data flows.
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@@ -25,4 +25,4 @@ where <code>δ_t = r_t + γV(s_{t+1}) - V(s_t)</code></p><ul><li><strong>γ (gam
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2016 -
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2025
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Eric X. Liu
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File diff suppressed because one or more lines are too long
@@ -7,7 +7,7 @@ That message is the tell: Secure Boot is enabled and the kernel refuses to load
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nvidia-smi failed to communicate with the NVIDIA driver modprobe nvidia → “Key was rejected by service” That message is the tell: Secure Boot is enabled and the kernel refuses to load modules not signed by a trusted key."><meta property="og:url" content="https://ericxliu.me/posts/secure-boot-dkms-and-mok-on-proxmox-debian/"><meta property="og:site_name" content="Eric X. Liu's Personal Page"><meta property="og:title" content="Fixing GPU Operator Pods Stuck in Init: Secure Boot, DKMS, and MOK on Proxmox + Debian"><meta property="og:description" content="I hit an issue where all GPU Operator pods on one node were stuck in Init after migrating from Legacy BIOS to UEFI. The common error was NVIDIA components waiting for “toolkit-ready,” while the toolkit init container looped with:
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nvidia-smi failed to communicate with the NVIDIA driver modprobe nvidia → “Key was rejected by service” That message is the tell: Secure Boot is enabled and the kernel refuses to load modules not signed by a trusted key."><meta property="og:locale" content="en"><meta property="og:type" content="article"><meta property="article:section" content="posts"><meta property="article:published_time" content="2025-08-09T00:00:00+00:00"><meta property="article:modified_time" content="2025-08-14T06:50:22+00:00"><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=canonical href=https://ericxliu.me/posts/secure-boot-dkms-and-mok-on-proxmox-debian/><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-regular-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=stylesheet href=/css/coder.min.f03d6359cf766772af14fbe07ce6aca734b321c2e15acba0bbf4e2254941c460.css integrity="sha256-8D1jWc92Z3KvFPvgfOaspzSzIcLhWsugu/TiJUlBxGA=" crossorigin=anonymous media=screen><link rel=stylesheet href=/css/coder-dark.min.a00e6364bacbc8266ad1cc81230774a1397198f8cfb7bcba29b7d6fcb54ce57f.css integrity="sha256-oA5jZLrLyCZq0cyBIwd0oTlxmPjPt7y6KbfW/LVM5X8=" crossorigin=anonymous media=screen><link rel=icon type=image/svg+xml href=/images/favicon.svg sizes=any><link rel=icon type=image/png href=/images/favicon-32x32.png sizes=32x32><link rel=icon type=image/png href=/images/favicon-16x16.png sizes=16x16><link rel=apple-touch-icon href=/images/apple-touch-icon.png><link rel=apple-touch-icon sizes=180x180 href=/images/apple-touch-icon.png><link rel=manifest href=/site.webmanifest><link rel=mask-icon href=/images/safari-pinned-tab.svg color=#5bbad5><script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-3972604619956476" crossorigin=anonymous></script><script type=application/ld+json>{"@context":"http://schema.org","@type":"Person","name":"Eric X. Liu","url":"https:\/\/ericxliu.me\/","description":"Software \u0026 Performance Engineer at Google","sameAs":["https:\/\/www.linkedin.com\/in\/eric-x-liu-46648b93\/","https:\/\/git.ericxliu.me\/eric"]}</script><script type=application/ld+json>{"@context":"http://schema.org","@type":"BlogPosting","headline":"Fixing GPU Operator Pods Stuck in Init: Secure Boot, DKMS, and MOK on Proxmox \u002b Debian","genre":"Blog","wordcount":"639","url":"https:\/\/ericxliu.me\/posts\/secure-boot-dkms-and-mok-on-proxmox-debian\/","datePublished":"2025-08-09T00:00:00\u002b00:00","dateModified":"2025-08-14T06:50:22\u002b00:00","description":"\u003cp\u003eI hit an issue where all GPU Operator pods on one node were stuck in Init after migrating from Legacy BIOS to UEFI. The common error was NVIDIA components waiting for “toolkit-ready,” while the toolkit init container looped with:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003envidia-smi failed to communicate with the NVIDIA driver\u003c\/li\u003e\n\u003cli\u003emodprobe nvidia → “Key was rejected by service”\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eThat message is the tell: Secure Boot is enabled and the kernel refuses to load modules not signed by a trusted key.\u003c\/p\u003e","author":{"@type":"Person","name":"Eric X. Liu"}}</script></head><body class="preload-transitions colorscheme-auto"><div class=float-container><a id=dark-mode-toggle class=colorscheme-toggle><i class="fa-solid fa-adjust fa-fw" aria-hidden=true></i></a></div><main class=wrapper><nav class=navigation><section class=container><a class=navigation-title href=https://ericxliu.me/>Eric X. Liu's Personal Page
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container post"><article><header><div class=post-title><h1 class=title><a class=title-link href=https://ericxliu.me/posts/secure-boot-dkms-and-mok-on-proxmox-debian/>Fixing GPU Operator Pods Stuck in Init: Secure Boot, DKMS, and MOK on Proxmox + Debian</a></h1></div><div class=post-meta><div class=date><span class=posted-on><i class="fa-solid fa-calendar" aria-hidden=true></i>
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/about/>About</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container post"><article><header><div class=post-title><h1 class=title><a class=title-link href=https://ericxliu.me/posts/secure-boot-dkms-and-mok-on-proxmox-debian/>Fixing GPU Operator Pods Stuck in Init: Secure Boot, DKMS, and MOK on Proxmox + Debian</a></h1></div><div class=post-meta><div class=date><span class=posted-on><i class="fa-solid fa-calendar" aria-hidden=true></i>
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<time datetime=2025-08-09T00:00:00Z>August 9, 2025
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</time></span><span class=reading-time><i class="fa-solid fa-clock" aria-hidden=true></i>
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3-minute read</span></div></div></header><div class=post-content><p>I hit an issue where all GPU Operator pods on one node were stuck in Init after migrating from Legacy BIOS to UEFI. The common error was NVIDIA components waiting for “toolkit-ready,” while the toolkit init container looped with:</p><ul><li>nvidia-smi failed to communicate with the NVIDIA driver</li><li>modprobe nvidia → “Key was rejected by service”</li></ul><p>That message is the tell: Secure Boot is enabled and the kernel refuses to load modules not signed by a trusted key.</p><h3 id=environment>Environment
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@@ -59,4 +59,4 @@ nvidia-smi failed to communicate with the NVIDIA driver modprobe nvidia → “K
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2025
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Eric X. Liu
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<a href="https://git.ericxliu.me/eric/ericxliu-me/commit/3f9f80d">[3f9f80d]</a></section></footer></main><script src=/js/coder.min.6ae284be93d2d19dad1f02b0039508d9aab3180a12a06dcc71b0b0ef7825a317.js integrity="sha256-auKEvpPS0Z2tHwKwA5UI2aqzGAoSoG3McbCw73gloxc="></script><script defer src=https://static.cloudflareinsights.com/beacon.min.js data-cf-beacon='{"token": "987638e636ce4dbb932d038af74c17d1"}'></script></body></html>
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<a href="https://git.ericxliu.me/eric/ericxliu-me/commit/2bb856b">[2bb856b]</a></section></footer></main><script src=/js/coder.min.6ae284be93d2d19dad1f02b0039508d9aab3180a12a06dcc71b0b0ef7825a317.js integrity="sha256-auKEvpPS0Z2tHwKwA5UI2aqzGAoSoG3McbCw73gloxc="></script><script defer src=https://static.cloudflareinsights.com/beacon.min.js data-cf-beacon='{"token": "987638e636ce4dbb932d038af74c17d1"}'></script></body></html>
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@@ -3,7 +3,7 @@ Supabase enters this space with a radically different philosophy: transparency.
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Supabase enters this space with a radically different philosophy: transparency. It provides the convenience of a BaaS, but it’s built on the world’s most trusted relational database: PostgreSQL. The “magic” isn’t a proprietary black box; it’s a carefully assembled suite of open-source tools that enhance Postgres, not hide it."><meta property="og:url" content="https://ericxliu.me/posts/supabase-deep-dive/"><meta property="og:site_name" content="Eric X. Liu's Personal Page"><meta property="og:title" content="Supabase Deep Dive: It's Not Magic, It's Just Postgres"><meta property="og:description" content="In the world of Backend-as-a-Service (BaaS), platforms are often treated as magic boxes. You push data in, you get data out, and you hope the magic inside scales. While this simplicity is powerful, it can obscure the underlying mechanics, leaving developers wondering what’s really going on.
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Supabase enters this space with a radically different philosophy: transparency. It provides the convenience of a BaaS, but it’s built on the world’s most trusted relational database: PostgreSQL. The “magic” isn’t a proprietary black box; it’s a carefully assembled suite of open-source tools that enhance Postgres, not hide it."><meta property="og:locale" content="en"><meta property="og:type" content="article"><meta property="article:section" content="posts"><meta property="article:published_time" content="2025-08-03T00:00:00+00:00"><meta property="article:modified_time" content="2025-08-04T03:59:37+00:00"><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=canonical href=https://ericxliu.me/posts/supabase-deep-dive/><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-regular-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=stylesheet href=/css/coder.min.f03d6359cf766772af14fbe07ce6aca734b321c2e15acba0bbf4e2254941c460.css integrity="sha256-8D1jWc92Z3KvFPvgfOaspzSzIcLhWsugu/TiJUlBxGA=" crossorigin=anonymous media=screen><link rel=stylesheet href=/css/coder-dark.min.a00e6364bacbc8266ad1cc81230774a1397198f8cfb7bcba29b7d6fcb54ce57f.css integrity="sha256-oA5jZLrLyCZq0cyBIwd0oTlxmPjPt7y6KbfW/LVM5X8=" crossorigin=anonymous media=screen><link rel=icon type=image/svg+xml href=/images/favicon.svg sizes=any><link rel=icon type=image/png href=/images/favicon-32x32.png sizes=32x32><link rel=icon type=image/png href=/images/favicon-16x16.png sizes=16x16><link rel=apple-touch-icon href=/images/apple-touch-icon.png><link rel=apple-touch-icon sizes=180x180 href=/images/apple-touch-icon.png><link rel=manifest href=/site.webmanifest><link rel=mask-icon href=/images/safari-pinned-tab.svg color=#5bbad5><script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-3972604619956476" crossorigin=anonymous></script><script type=application/ld+json>{"@context":"http://schema.org","@type":"Person","name":"Eric X. Liu","url":"https:\/\/ericxliu.me\/","description":"Software \u0026 Performance Engineer at Google","sameAs":["https:\/\/www.linkedin.com\/in\/eric-x-liu-46648b93\/","https:\/\/git.ericxliu.me\/eric"]}</script><script type=application/ld+json>{"@context":"http://schema.org","@type":"BlogPosting","headline":"Supabase Deep Dive: It\u0027s Not Magic, It\u0027s Just Postgres","genre":"Blog","wordcount":"1513","url":"https:\/\/ericxliu.me\/posts\/supabase-deep-dive\/","datePublished":"2025-08-03T00:00:00\u002b00:00","dateModified":"2025-08-04T03:59:37\u002b00:00","description":"\u003cp\u003eIn the world of Backend-as-a-Service (BaaS), platforms are often treated as magic boxes. You push data in, you get data out, and you hope the magic inside scales. While this simplicity is powerful, it can obscure the underlying mechanics, leaving developers wondering what\u0026rsquo;s really going on.\u003c\/p\u003e\n\u003cp\u003eSupabase enters this space with a radically different philosophy: \u003cstrong\u003etransparency\u003c\/strong\u003e. It provides the convenience of a BaaS, but it’s built on the world\u0026rsquo;s most trusted relational database: PostgreSQL. The \u0026ldquo;magic\u0026rdquo; isn\u0026rsquo;t a proprietary black box; it\u0026rsquo;s a carefully assembled suite of open-source tools that enhance Postgres, not hide it.\u003c\/p\u003e","author":{"@type":"Person","name":"Eric X. Liu"}}</script></head><body class="preload-transitions colorscheme-auto"><div class=float-container><a id=dark-mode-toggle class=colorscheme-toggle><i class="fa-solid fa-adjust fa-fw" aria-hidden=true></i></a></div><main class=wrapper><nav class=navigation><section class=container><a class=navigation-title href=https://ericxliu.me/>Eric X. Liu's Personal Page
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container post"><article><header><div class=post-title><h1 class=title><a class=title-link href=https://ericxliu.me/posts/supabase-deep-dive/>Supabase Deep Dive: It's Not Magic, It's Just Postgres</a></h1></div><div class=post-meta><div class=date><span class=posted-on><i class="fa-solid fa-calendar" aria-hidden=true></i>
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/about/>About</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container post"><article><header><div class=post-title><h1 class=title><a class=title-link href=https://ericxliu.me/posts/supabase-deep-dive/>Supabase Deep Dive: It's Not Magic, It's Just Postgres</a></h1></div><div class=post-meta><div class=date><span class=posted-on><i class="fa-solid fa-calendar" aria-hidden=true></i>
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<time datetime=2025-08-03T00:00:00Z>August 3, 2025
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</time></span><span class=reading-time><i class="fa-solid fa-clock" aria-hidden=true></i>
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8-minute read</span></div></div></header><div class=post-content><p>In the world of Backend-as-a-Service (BaaS), platforms are often treated as magic boxes. You push data in, you get data out, and you hope the magic inside scales. While this simplicity is powerful, it can obscure the underlying mechanics, leaving developers wondering what’s really going on.</p><p>Supabase enters this space with a radically different philosophy: <strong>transparency</strong>. It provides the convenience of a BaaS, but it’s built on the world’s most trusted relational database: PostgreSQL. The “magic” isn’t a proprietary black box; it’s a carefully assembled suite of open-source tools that enhance Postgres, not hide it.</p><p>This deep dive will deconstruct that suite. We will move beyond the basics to explore the architectural patterns, security models, and development workflows that allow you to build robust, scalable applications. We will cover:</p><ul><li><strong>The Supabase Blueprint:</strong> A procedural guide to designing your application.</li><li><strong>The Pillars of Supabase:</strong> A detailed look at Auth, Storage, Functions, and Realtime.</li><li><strong>Transactional Realtime:</strong> How Supabase guarantees data consistency in a live environment.</li><li><strong>Best Practices:</strong> The practical knowledge you need before writing a single line of code.</li></ul><h3 id=the-guiding-philosophy-your-database-as-the-source-of-truth>The Guiding Philosophy: Your Database as the Source of Truth
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@@ -90,4 +90,4 @@ Supabase enters this space with a radically different philosophy: transparency.
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2016 -
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2025
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Eric X. Liu
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@@ -3,7 +3,7 @@ But to truly understand the field, we must look at the pivotal models that explo
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But to truly understand the field, we must look at the pivotal models that explored different paths. Google’s T5, or Text-to-Text Transfer Transformer, stands out as one of the most influential. It didn’t just introduce a new model; it proposed a new philosophy. This article dives deep into the architecture of T5, how it fundamentally differs from modern LLMs, and the lasting legacy of its unique design choices."><meta property="og:url" content="https://ericxliu.me/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/"><meta property="og:site_name" content="Eric X. Liu's Personal Page"><meta property="og:title" content="An Architectural Deep Dive of T5"><meta property="og:description" content="In the rapidly evolving landscape of Large Language Models, a few key architectures define the dominant paradigms. Today, the “decoder-only” model, popularized by the GPT series and its successors like LLaMA and Mistral, reigns supreme. These models are scaled to incredible sizes and excel at in-context learning.
|
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But to truly understand the field, we must look at the pivotal models that explored different paths. Google’s T5, or Text-to-Text Transfer Transformer, stands out as one of the most influential. It didn’t just introduce a new model; it proposed a new philosophy. This article dives deep into the architecture of T5, how it fundamentally differs from modern LLMs, and the lasting legacy of its unique design choices."><meta property="og:locale" content="en"><meta property="og:type" content="article"><meta property="article:section" content="posts"><meta property="article:published_time" content="2025-06-01T00:00:00+00:00"><meta property="article:modified_time" content="2025-08-03T03:41:10+00:00"><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=canonical href=https://ericxliu.me/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-regular-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=stylesheet href=/css/coder.min.f03d6359cf766772af14fbe07ce6aca734b321c2e15acba0bbf4e2254941c460.css integrity="sha256-8D1jWc92Z3KvFPvgfOaspzSzIcLhWsugu/TiJUlBxGA=" crossorigin=anonymous media=screen><link rel=stylesheet href=/css/coder-dark.min.a00e6364bacbc8266ad1cc81230774a1397198f8cfb7bcba29b7d6fcb54ce57f.css integrity="sha256-oA5jZLrLyCZq0cyBIwd0oTlxmPjPt7y6KbfW/LVM5X8=" crossorigin=anonymous media=screen><link rel=icon type=image/svg+xml href=/images/favicon.svg sizes=any><link rel=icon type=image/png href=/images/favicon-32x32.png sizes=32x32><link rel=icon type=image/png href=/images/favicon-16x16.png sizes=16x16><link rel=apple-touch-icon href=/images/apple-touch-icon.png><link rel=apple-touch-icon sizes=180x180 href=/images/apple-touch-icon.png><link rel=manifest href=/site.webmanifest><link rel=mask-icon href=/images/safari-pinned-tab.svg color=#5bbad5><script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-3972604619956476" crossorigin=anonymous></script><script type=application/ld+json>{"@context":"http://schema.org","@type":"Person","name":"Eric X. Liu","url":"https:\/\/ericxliu.me\/","description":"Software \u0026 Performance Engineer at Google","sameAs":["https:\/\/www.linkedin.com\/in\/eric-x-liu-46648b93\/","https:\/\/git.ericxliu.me\/eric"]}</script><script type=application/ld+json>{"@context":"http://schema.org","@type":"BlogPosting","headline":"An Architectural Deep Dive of T5","genre":"Blog","wordcount":"1183","url":"https:\/\/ericxliu.me\/posts\/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive\/","datePublished":"2025-06-01T00:00:00\u002b00:00","dateModified":"2025-08-03T03:41:10\u002b00:00","description":"\u003cp\u003eIn the rapidly evolving landscape of Large Language Models, a few key architectures define the dominant paradigms. Today, the \u0026ldquo;decoder-only\u0026rdquo; model, popularized by the GPT series and its successors like LLaMA and Mistral, reigns supreme. These models are scaled to incredible sizes and excel at in-context learning.\u003c\/p\u003e\n\u003cp\u003eBut to truly understand the field, we must look at the pivotal models that explored different paths. Google\u0026rsquo;s T5, or \u003cstrong\u003eText-to-Text Transfer Transformer\u003c\/strong\u003e, stands out as one of the most influential. It didn\u0026rsquo;t just introduce a new model; it proposed a new philosophy. This article dives deep into the architecture of T5, how it fundamentally differs from modern LLMs, and the lasting legacy of its unique design choices.\u003c\/p\u003e","author":{"@type":"Person","name":"Eric X. Liu"}}</script></head><body class="preload-transitions colorscheme-auto"><div class=float-container><a id=dark-mode-toggle class=colorscheme-toggle><i class="fa-solid fa-adjust fa-fw" aria-hidden=true></i></a></div><main class=wrapper><nav class=navigation><section class=container><a class=navigation-title href=https://ericxliu.me/>Eric X. Liu's Personal Page
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container post"><article><header><div class=post-title><h1 class=title><a class=title-link href=https://ericxliu.me/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/>An Architectural Deep Dive of T5</a></h1></div><div class=post-meta><div class=date><span class=posted-on><i class="fa-solid fa-calendar" aria-hidden=true></i>
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/about/>About</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container post"><article><header><div class=post-title><h1 class=title><a class=title-link href=https://ericxliu.me/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/>An Architectural Deep Dive of T5</a></h1></div><div class=post-meta><div class=date><span class=posted-on><i class="fa-solid fa-calendar" aria-hidden=true></i>
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<time datetime=2025-06-01T00:00:00Z>June 1, 2025
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</time></span><span class=reading-time><i class="fa-solid fa-clock" aria-hidden=true></i>
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6-minute read</span></div></div></header><div class=post-content><p>In the rapidly evolving landscape of Large Language Models, a few key architectures define the dominant paradigms. Today, the “decoder-only” model, popularized by the GPT series and its successors like LLaMA and Mistral, reigns supreme. These models are scaled to incredible sizes and excel at in-context learning.</p><p>But to truly understand the field, we must look at the pivotal models that explored different paths. Google’s T5, or <strong>Text-to-Text Transfer Transformer</strong>, stands out as one of the most influential. It didn’t just introduce a new model; it proposed a new philosophy. This article dives deep into the architecture of T5, how it fundamentally differs from modern LLMs, and the lasting legacy of its unique design choices.</p><h3 id=the-core-philosophy-everything-is-a-text-to-text-problem>The Core Philosophy: Everything is a Text-to-Text Problem
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@@ -30,4 +30,4 @@ But to truly understand the field, we must look at the pivotal models that explo
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2016 -
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2025
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Eric X. Liu
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<a href="https://git.ericxliu.me/eric/ericxliu-me/commit/3f9f80d">[3f9f80d]</a></section></footer></main><script src=/js/coder.min.6ae284be93d2d19dad1f02b0039508d9aab3180a12a06dcc71b0b0ef7825a317.js integrity="sha256-auKEvpPS0Z2tHwKwA5UI2aqzGAoSoG3McbCw73gloxc="></script><script defer src=https://static.cloudflareinsights.com/beacon.min.js data-cf-beacon='{"token": "987638e636ce4dbb932d038af74c17d1"}'></script></body></html>
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@@ -3,7 +3,7 @@ This article explores the mathematical equivalence between Hinton’s concept of
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This article explores the mathematical equivalence between Hinton’s concept of Fast Weights as Associative Memory and the recurrence mechanisms found in models such as Mamba and RWKV."><meta property="og:url" content="https://ericxliu.me/posts/the-convergence-of-fast-weights-linear-attention-and-state-space-models/"><meta property="og:site_name" content="Eric X. Liu's Personal Page"><meta property="og:title" content="The Convergence of Fast Weights, Linear Attention, and State Space Models"><meta property="og:description" content="Modern Large Language Models (LLMs) are dominated by the Transformer architecture. However, as context windows grow, the computational cost of the Transformer’s attention mechanism has become a primary bottleneck. Recent discussions in the AI community—most notably by Geoffrey Hinton—have highlighted a theoretical link between biological memory mechanisms (“Fast Weights”) and efficient engineering solutions like Linear Transformers and State Space Models (SSMs).
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This article explores the mathematical equivalence between Hinton’s concept of Fast Weights as Associative Memory and the recurrence mechanisms found in models such as Mamba and RWKV."><meta property="og:locale" content="en"><meta property="og:type" content="article"><meta property="article:section" content="posts"><meta property="article:published_time" content="2025-12-19T00:00:00+00:00"><meta property="article:modified_time" content="2025-12-19T21:21:55+00:00"><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=canonical href=https://ericxliu.me/posts/the-convergence-of-fast-weights-linear-attention-and-state-space-models/><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-regular-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=stylesheet href=/css/coder.min.f03d6359cf766772af14fbe07ce6aca734b321c2e15acba0bbf4e2254941c460.css integrity="sha256-8D1jWc92Z3KvFPvgfOaspzSzIcLhWsugu/TiJUlBxGA=" crossorigin=anonymous media=screen><link rel=stylesheet href=/css/coder-dark.min.a00e6364bacbc8266ad1cc81230774a1397198f8cfb7bcba29b7d6fcb54ce57f.css integrity="sha256-oA5jZLrLyCZq0cyBIwd0oTlxmPjPt7y6KbfW/LVM5X8=" crossorigin=anonymous media=screen><link rel=icon type=image/svg+xml href=/images/favicon.svg sizes=any><link rel=icon type=image/png href=/images/favicon-32x32.png sizes=32x32><link rel=icon type=image/png href=/images/favicon-16x16.png sizes=16x16><link rel=apple-touch-icon href=/images/apple-touch-icon.png><link rel=apple-touch-icon sizes=180x180 href=/images/apple-touch-icon.png><link rel=manifest href=/site.webmanifest><link rel=mask-icon href=/images/safari-pinned-tab.svg color=#5bbad5><script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-3972604619956476" crossorigin=anonymous></script><script type=application/ld+json>{"@context":"http://schema.org","@type":"Person","name":"Eric X. Liu","url":"https:\/\/ericxliu.me\/","description":"Software \u0026 Performance Engineer at Google","sameAs":["https:\/\/www.linkedin.com\/in\/eric-x-liu-46648b93\/","https:\/\/git.ericxliu.me\/eric"]}</script><script type=application/ld+json>{"@context":"http://schema.org","@type":"BlogPosting","headline":"The Convergence of Fast Weights, Linear Attention, and State Space Models","genre":"Blog","wordcount":"984","url":"https:\/\/ericxliu.me\/posts\/the-convergence-of-fast-weights-linear-attention-and-state-space-models\/","datePublished":"2025-12-19T00:00:00\u002b00:00","dateModified":"2025-12-19T21:21:55\u002b00:00","description":"\u003cp\u003eModern Large Language Models (LLMs) are dominated by the Transformer architecture. However, as context windows grow, the computational cost of the Transformer’s attention mechanism has become a primary bottleneck. Recent discussions in the AI community—most notably by Geoffrey Hinton—have highlighted a theoretical link between biological memory mechanisms (\u0026ldquo;Fast Weights\u0026rdquo;) and efficient engineering solutions like Linear Transformers and State Space Models (SSMs).\u003c\/p\u003e\n\u003cp\u003eThis article explores the mathematical equivalence between Hinton’s concept of Fast Weights as Associative Memory and the recurrence mechanisms found in models such as Mamba and RWKV.\u003c\/p\u003e","author":{"@type":"Person","name":"Eric X. Liu"}}</script></head><body class="preload-transitions colorscheme-auto"><div class=float-container><a id=dark-mode-toggle class=colorscheme-toggle><i class="fa-solid fa-adjust fa-fw" aria-hidden=true></i></a></div><main class=wrapper><nav class=navigation><section class=container><a class=navigation-title href=https://ericxliu.me/>Eric X. Liu's Personal Page
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container post"><article><header><div class=post-title><h1 class=title><a class=title-link href=https://ericxliu.me/posts/the-convergence-of-fast-weights-linear-attention-and-state-space-models/>The Convergence of Fast Weights, Linear Attention, and State Space Models</a></h1></div><div class=post-meta><div class=date><span class=posted-on><i class="fa-solid fa-calendar" aria-hidden=true></i>
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/about/>About</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container post"><article><header><div class=post-title><h1 class=title><a class=title-link href=https://ericxliu.me/posts/the-convergence-of-fast-weights-linear-attention-and-state-space-models/>The Convergence of Fast Weights, Linear Attention, and State Space Models</a></h1></div><div class=post-meta><div class=date><span class=posted-on><i class="fa-solid fa-calendar" aria-hidden=true></i>
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<time datetime=2025-12-19T00:00:00Z>December 19, 2025
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</time></span><span class=reading-time><i class="fa-solid fa-clock" aria-hidden=true></i>
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5-minute read</span></div></div></header><div class=post-content><p>Modern Large Language Models (LLMs) are dominated by the Transformer architecture. However, as context windows grow, the computational cost of the Transformer’s attention mechanism has become a primary bottleneck. Recent discussions in the AI community—most notably by Geoffrey Hinton—have highlighted a theoretical link between biological memory mechanisms (“Fast Weights”) and efficient engineering solutions like Linear Transformers and State Space Models (SSMs).</p><p>This article explores the mathematical equivalence between Hinton’s concept of Fast Weights as Associative Memory and the recurrence mechanisms found in models such as Mamba and RWKV.</p><h2 id=1-the-standard-transformer-bottleneck>1. The Standard Transformer Bottleneck
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@@ -26,4 +26,4 @@ This article explores the mathematical equivalence between Hinton’s concept of
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2016 -
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2025
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Eric X. Liu
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<a href="https://git.ericxliu.me/eric/ericxliu-me/commit/3f9f80d">[3f9f80d]</a></section></footer></main><script src=/js/coder.min.6ae284be93d2d19dad1f02b0039508d9aab3180a12a06dcc71b0b0ef7825a317.js integrity="sha256-auKEvpPS0Z2tHwKwA5UI2aqzGAoSoG3McbCw73gloxc="></script><script defer src=https://static.cloudflareinsights.com/beacon.min.js data-cf-beacon='{"token": "987638e636ce4dbb932d038af74c17d1"}'></script></body></html>
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@@ -10,7 +10,7 @@ In deep learning, a “channel” can be thought of as a feature dimensi
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1. The “Channel”: A Foundational View of d_model Link to heading In deep learning, a “channel” can be thought of as a feature dimension. While this term is common in Convolutional Neural Networks for images (e.g., Red, Green, Blue channels), in LLMs, the analogous concept is the model’s primary embedding dimension, commonly referred to as d_model."><meta property="og:url" content="https://ericxliu.me/posts/transformer-s-core-mechanics/"><meta property="og:site_name" content="Eric X. Liu's Personal Page"><meta property="og:title" content="Transformer's Core Mechanics"><meta property="og:description" content="The Transformer architecture is the bedrock of modern Large Language Models (LLMs). While its high-level success is widely known, a deeper understanding requires dissecting its core components. This article provides a detailed, technical breakdown of the fundamental concepts within a Transformer block, from the notion of “channels” to the intricate workings of the attention mechanism and its relationship with other advanced architectures like Mixture of Experts.
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1. The “Channel”: A Foundational View of d_model Link to heading In deep learning, a “channel” can be thought of as a feature dimension. While this term is common in Convolutional Neural Networks for images (e.g., Red, Green, Blue channels), in LLMs, the analogous concept is the model’s primary embedding dimension, commonly referred to as d_model."><meta property="og:locale" content="en"><meta property="og:type" content="article"><meta property="article:section" content="posts"><meta property="article:published_time" content="2025-04-01T00:00:00+00:00"><meta property="article:modified_time" content="2025-10-02T08:42:39+00:00"><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=canonical href=https://ericxliu.me/posts/transformer-s-core-mechanics/><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-regular-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=stylesheet href=/css/coder.min.f03d6359cf766772af14fbe07ce6aca734b321c2e15acba0bbf4e2254941c460.css integrity="sha256-8D1jWc92Z3KvFPvgfOaspzSzIcLhWsugu/TiJUlBxGA=" crossorigin=anonymous media=screen><link rel=stylesheet href=/css/coder-dark.min.a00e6364bacbc8266ad1cc81230774a1397198f8cfb7bcba29b7d6fcb54ce57f.css integrity="sha256-oA5jZLrLyCZq0cyBIwd0oTlxmPjPt7y6KbfW/LVM5X8=" crossorigin=anonymous media=screen><link rel=icon type=image/svg+xml href=/images/favicon.svg sizes=any><link rel=icon type=image/png href=/images/favicon-32x32.png sizes=32x32><link rel=icon type=image/png href=/images/favicon-16x16.png sizes=16x16><link rel=apple-touch-icon href=/images/apple-touch-icon.png><link rel=apple-touch-icon sizes=180x180 href=/images/apple-touch-icon.png><link rel=manifest href=/site.webmanifest><link rel=mask-icon href=/images/safari-pinned-tab.svg color=#5bbad5><script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-3972604619956476" crossorigin=anonymous></script><script type=application/ld+json>{"@context":"http://schema.org","@type":"Person","name":"Eric X. Liu","url":"https:\/\/ericxliu.me\/","description":"Software \u0026 Performance Engineer at Google","sameAs":["https:\/\/www.linkedin.com\/in\/eric-x-liu-46648b93\/","https:\/\/git.ericxliu.me\/eric"]}</script><script type=application/ld+json>{"@context":"http://schema.org","@type":"BlogPosting","headline":"Transformer\u0027s Core Mechanics","genre":"Blog","wordcount":"1326","url":"https:\/\/ericxliu.me\/posts\/transformer-s-core-mechanics\/","datePublished":"2025-04-01T00:00:00\u002b00:00","dateModified":"2025-10-02T08:42:39\u002b00:00","description":"\u003cp\u003eThe Transformer architecture is the bedrock of modern Large Language Models (LLMs). While its high-level success is widely known, a deeper understanding requires dissecting its core components. This article provides a detailed, technical breakdown of the fundamental concepts within a Transformer block, from the notion of \u0026ldquo;channels\u0026rdquo; to the intricate workings of the attention mechanism and its relationship with other advanced architectures like Mixture of Experts.\u003c\/p\u003e\n\u003ch3 id=\u00221-the-channel-a-foundational-view-of-d_model\u0022\u003e\n 1. The \u0026ldquo;Channel\u0026rdquo;: A Foundational View of \u003ccode\u003ed_model\u003c\/code\u003e\n \u003ca class=\u0022heading-link\u0022 href=\u0022#1-the-channel-a-foundational-view-of-d_model\u0022\u003e\n \u003ci class=\u0022fa-solid fa-link\u0022 aria-hidden=\u0022true\u0022 title=\u0022Link to heading\u0022\u003e\u003c\/i\u003e\n \u003cspan class=\u0022sr-only\u0022\u003eLink to heading\u003c\/span\u003e\n \u003c\/a\u003e\n\u003c\/h3\u003e\n\u003cp\u003eIn deep learning, a \u0026ldquo;channel\u0026rdquo; can be thought of as a feature dimension. While this term is common in Convolutional Neural Networks for images (e.g., Red, Green, Blue channels), in LLMs, the analogous concept is the model\u0026rsquo;s primary embedding dimension, commonly referred to as \u003ccode\u003ed_model\u003c\/code\u003e.\u003c\/p\u003e","author":{"@type":"Person","name":"Eric X. Liu"}}</script></head><body class="preload-transitions colorscheme-auto"><div class=float-container><a id=dark-mode-toggle class=colorscheme-toggle><i class="fa-solid fa-adjust fa-fw" aria-hidden=true></i></a></div><main class=wrapper><nav class=navigation><section class=container><a class=navigation-title href=https://ericxliu.me/>Eric X. Liu's Personal Page
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container post"><article><header><div class=post-title><h1 class=title><a class=title-link href=https://ericxliu.me/posts/transformer-s-core-mechanics/>Transformer's Core Mechanics</a></h1></div><div class=post-meta><div class=date><span class=posted-on><i class="fa-solid fa-calendar" aria-hidden=true></i>
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/about/>About</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container post"><article><header><div class=post-title><h1 class=title><a class=title-link href=https://ericxliu.me/posts/transformer-s-core-mechanics/>Transformer's Core Mechanics</a></h1></div><div class=post-meta><div class=date><span class=posted-on><i class="fa-solid fa-calendar" aria-hidden=true></i>
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<time datetime=2025-04-01T00:00:00Z>April 1, 2025
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</time></span><span class=reading-time><i class="fa-solid fa-clock" aria-hidden=true></i>
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7-minute read</span></div></div></header><div class=post-content><p>The Transformer architecture is the bedrock of modern Large Language Models (LLMs). While its high-level success is widely known, a deeper understanding requires dissecting its core components. This article provides a detailed, technical breakdown of the fundamental concepts within a Transformer block, from the notion of “channels” to the intricate workings of the attention mechanism and its relationship with other advanced architectures like Mixture of Experts.</p><h3 id=1-the-channel-a-foundational-view-of-d_model>1. The “Channel”: A Foundational View of <code>d_model</code>
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@@ -36,4 +36,4 @@ In deep learning, a “channel” can be thought of as a feature dimensi
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2016 -
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2025
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Eric X. Liu
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<a href="https://git.ericxliu.me/eric/ericxliu-me/commit/3f9f80d">[3f9f80d]</a></section></footer></main><script src=/js/coder.min.6ae284be93d2d19dad1f02b0039508d9aab3180a12a06dcc71b0b0ef7825a317.js integrity="sha256-auKEvpPS0Z2tHwKwA5UI2aqzGAoSoG3McbCw73gloxc="></script><script defer src=https://static.cloudflareinsights.com/beacon.min.js data-cf-beacon='{"token": "987638e636ce4dbb932d038af74c17d1"}'></script></body></html>
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<a href="https://git.ericxliu.me/eric/ericxliu-me/commit/2bb856b">[2bb856b]</a></section></footer></main><script src=/js/coder.min.6ae284be93d2d19dad1f02b0039508d9aab3180a12a06dcc71b0b0ef7825a317.js integrity="sha256-auKEvpPS0Z2tHwKwA5UI2aqzGAoSoG3McbCw73gloxc="></script><script defer src=https://static.cloudflareinsights.com/beacon.min.js data-cf-beacon='{"token": "987638e636ce4dbb932d038af74c17d1"}'></script></body></html>
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@@ -3,7 +3,7 @@ This article documents that journey. It details the pitfalls encountered, the co
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This article documents that journey. It details the pitfalls encountered, the core networking concepts that were essential to understand, and the best practices that ultimately led to a stable, secure, and logical network design built on a zone-based firewall model."><meta property="og:url" content="https://ericxliu.me/posts/unifi-vlan-migration-to-zone-based-architecture/"><meta property="og:site_name" content="Eric X. Liu's Personal Page"><meta property="og:title" content="UniFi VLAN Migration to Zone-Based Architecture"><meta property="og:description" content="Embarking on a network migration to a properly segmented VLAN architecture is a rite of passage for any serious home lab or small business operator. The goal is clear: improve security and organization by separating traffic. However, the path from a flat network to a segmented one is often paved with subtle but critical configuration details that can lead to hours of frustrating troubleshooting.
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This article documents that journey. It details the pitfalls encountered, the core networking concepts that were essential to understand, and the best practices that ultimately led to a stable, secure, and logical network design built on a zone-based firewall model."><meta property="og:locale" content="en"><meta property="og:type" content="article"><meta property="article:section" content="posts"><meta property="article:published_time" content="2025-09-22T00:00:00+00:00"><meta property="article:modified_time" content="2025-10-02T08:42:39+00:00"><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=canonical href=https://ericxliu.me/posts/unifi-vlan-migration-to-zone-based-architecture/><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-regular-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=stylesheet href=/css/coder.min.f03d6359cf766772af14fbe07ce6aca734b321c2e15acba0bbf4e2254941c460.css integrity="sha256-8D1jWc92Z3KvFPvgfOaspzSzIcLhWsugu/TiJUlBxGA=" crossorigin=anonymous media=screen><link rel=stylesheet href=/css/coder-dark.min.a00e6364bacbc8266ad1cc81230774a1397198f8cfb7bcba29b7d6fcb54ce57f.css integrity="sha256-oA5jZLrLyCZq0cyBIwd0oTlxmPjPt7y6KbfW/LVM5X8=" crossorigin=anonymous media=screen><link rel=icon type=image/svg+xml href=/images/favicon.svg sizes=any><link rel=icon type=image/png href=/images/favicon-32x32.png sizes=32x32><link rel=icon type=image/png href=/images/favicon-16x16.png sizes=16x16><link rel=apple-touch-icon href=/images/apple-touch-icon.png><link rel=apple-touch-icon sizes=180x180 href=/images/apple-touch-icon.png><link rel=manifest href=/site.webmanifest><link rel=mask-icon href=/images/safari-pinned-tab.svg color=#5bbad5><script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-3972604619956476" crossorigin=anonymous></script><script type=application/ld+json>{"@context":"http://schema.org","@type":"Person","name":"Eric X. Liu","url":"https:\/\/ericxliu.me\/","description":"Software \u0026 Performance Engineer at Google","sameAs":["https:\/\/www.linkedin.com\/in\/eric-x-liu-46648b93\/","https:\/\/git.ericxliu.me\/eric"]}</script><script type=application/ld+json>{"@context":"http://schema.org","@type":"BlogPosting","headline":"UniFi VLAN Migration to Zone-Based Architecture","genre":"Blog","wordcount":"1001","url":"https:\/\/ericxliu.me\/posts\/unifi-vlan-migration-to-zone-based-architecture\/","datePublished":"2025-09-22T00:00:00\u002b00:00","dateModified":"2025-10-02T08:42:39\u002b00:00","description":"\u003cp\u003eEmbarking on a network migration to a properly segmented VLAN architecture is a rite of passage for any serious home lab or small business operator. The goal is clear: improve security and organization by separating traffic. However, the path from a flat network to a segmented one is often paved with subtle but critical configuration details that can lead to hours of frustrating troubleshooting.\u003c\/p\u003e\n\u003cp\u003eThis article documents that journey. It details the pitfalls encountered, the core networking concepts that were essential to understand, and the best practices that ultimately led to a stable, secure, and logical network design built on a zone-based firewall model.\u003c\/p\u003e","author":{"@type":"Person","name":"Eric X. Liu"}}</script></head><body class="preload-transitions colorscheme-auto"><div class=float-container><a id=dark-mode-toggle class=colorscheme-toggle><i class="fa-solid fa-adjust fa-fw" aria-hidden=true></i></a></div><main class=wrapper><nav class=navigation><section class=container><a class=navigation-title href=https://ericxliu.me/>Eric X. Liu's Personal Page
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container post"><article><header><div class=post-title><h1 class=title><a class=title-link href=https://ericxliu.me/posts/unifi-vlan-migration-to-zone-based-architecture/>UniFi VLAN Migration to Zone-Based Architecture</a></h1></div><div class=post-meta><div class=date><span class=posted-on><i class="fa-solid fa-calendar" aria-hidden=true></i>
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/about/>About</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container post"><article><header><div class=post-title><h1 class=title><a class=title-link href=https://ericxliu.me/posts/unifi-vlan-migration-to-zone-based-architecture/>UniFi VLAN Migration to Zone-Based Architecture</a></h1></div><div class=post-meta><div class=date><span class=posted-on><i class="fa-solid fa-calendar" aria-hidden=true></i>
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<time datetime=2025-09-22T00:00:00Z>September 22, 2025
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</time></span><span class=reading-time><i class="fa-solid fa-clock" aria-hidden=true></i>
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5-minute read</span></div></div></header><div class=post-content><p>Embarking on a network migration to a properly segmented VLAN architecture is a rite of passage for any serious home lab or small business operator. The goal is clear: improve security and organization by separating traffic. However, the path from a flat network to a segmented one is often paved with subtle but critical configuration details that can lead to hours of frustrating troubleshooting.</p><p>This article documents that journey. It details the pitfalls encountered, the core networking concepts that were essential to understand, and the best practices that ultimately led to a stable, secure, and logical network design built on a zone-based firewall model.</p><h3 id=lesson-1-demystifying-the-native-vlan>Lesson 1: Demystifying the Native VLAN
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@@ -28,4 +28,4 @@ This article documents that journey. It details the pitfalls encountered, the co
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Eric X. Liu
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<a href="https://git.ericxliu.me/eric/ericxliu-me/commit/3f9f80d">[3f9f80d]</a></section></footer></main><script src=/js/coder.min.6ae284be93d2d19dad1f02b0039508d9aab3180a12a06dcc71b0b0ef7825a317.js integrity="sha256-auKEvpPS0Z2tHwKwA5UI2aqzGAoSoG3McbCw73gloxc="></script><script defer src=https://static.cloudflareinsights.com/beacon.min.js data-cf-beacon='{"token": "987638e636ce4dbb932d038af74c17d1"}'></script></body></html>
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<a href="https://git.ericxliu.me/eric/ericxliu-me/commit/2bb856b">[2bb856b]</a></section></footer></main><script src=/js/coder.min.6ae284be93d2d19dad1f02b0039508d9aab3180a12a06dcc71b0b0ef7825a317.js integrity="sha256-auKEvpPS0Z2tHwKwA5UI2aqzGAoSoG3McbCw73gloxc="></script><script defer src=https://static.cloudflareinsights.com/beacon.min.js data-cf-beacon='{"token": "987638e636ce4dbb932d038af74c17d1"}'></script></body></html>
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rootCA.pem
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@@ -10,7 +10,7 @@ Prior to PagedAttention, systems allocated contiguous memory for the maximum pos
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The Status Quo: PagedAttention and Software Tables Link to heading Prior to PagedAttention, systems allocated contiguous memory for the maximum possible context length, leading to severe fragmentation and wasted memory. PagedAttention addressed this by chunking the KV cache into non-contiguous blocks, managed by a software-defined “page table” (the Block Table) [1]."><meta property="og:url" content="https://ericxliu.me/posts/vattention/"><meta property="og:site_name" content="Eric X. Liu's Personal Page"><meta property="og:title" content="vAttention"><meta property="og:description" content="Large Language Model (LLM) inference is memory-bound, primarily due to the Key-Value (KV) cache—a store of intermediate state that grows linearly with sequence length. Efficient management of this memory is critical for throughput. While PagedAttention (popularized by vLLM) became the industry standard by solving memory fragmentation via software, recent research suggests that leveraging the GPU’s native hardware Memory Management Unit (MMU) offers a more performant and portable solution.
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The Status Quo: PagedAttention and Software Tables Link to heading Prior to PagedAttention, systems allocated contiguous memory for the maximum possible context length, leading to severe fragmentation and wasted memory. PagedAttention addressed this by chunking the KV cache into non-contiguous blocks, managed by a software-defined “page table” (the Block Table) [1]."><meta property="og:locale" content="en"><meta property="og:type" content="article"><meta property="article:section" content="posts"><meta property="article:published_time" content="2025-12-08T00:00:00+00:00"><meta property="article:modified_time" content="2025-12-19T21:21:55+00:00"><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=canonical href=https://ericxliu.me/posts/vattention/><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-regular-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=stylesheet href=/css/coder.min.f03d6359cf766772af14fbe07ce6aca734b321c2e15acba0bbf4e2254941c460.css integrity="sha256-8D1jWc92Z3KvFPvgfOaspzSzIcLhWsugu/TiJUlBxGA=" crossorigin=anonymous media=screen><link rel=stylesheet href=/css/coder-dark.min.a00e6364bacbc8266ad1cc81230774a1397198f8cfb7bcba29b7d6fcb54ce57f.css integrity="sha256-oA5jZLrLyCZq0cyBIwd0oTlxmPjPt7y6KbfW/LVM5X8=" crossorigin=anonymous media=screen><link rel=icon type=image/svg+xml href=/images/favicon.svg sizes=any><link rel=icon type=image/png href=/images/favicon-32x32.png sizes=32x32><link rel=icon type=image/png href=/images/favicon-16x16.png sizes=16x16><link rel=apple-touch-icon href=/images/apple-touch-icon.png><link rel=apple-touch-icon sizes=180x180 href=/images/apple-touch-icon.png><link rel=manifest href=/site.webmanifest><link rel=mask-icon href=/images/safari-pinned-tab.svg color=#5bbad5><script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-3972604619956476" crossorigin=anonymous></script><script type=application/ld+json>{"@context":"http://schema.org","@type":"Person","name":"Eric X. Liu","url":"https:\/\/ericxliu.me\/","description":"Software \u0026 Performance Engineer at Google","sameAs":["https:\/\/www.linkedin.com\/in\/eric-x-liu-46648b93\/","https:\/\/git.ericxliu.me\/eric"]}</script><script type=application/ld+json>{"@context":"http://schema.org","@type":"BlogPosting","headline":"vAttention","genre":"Blog","wordcount":"824","url":"https:\/\/ericxliu.me\/posts\/vattention\/","datePublished":"2025-12-08T00:00:00\u002b00:00","dateModified":"2025-12-19T21:21:55\u002b00:00","description":"\u003cp\u003eLarge Language Model (LLM) inference is memory-bound, primarily due to the Key-Value (KV) cache—a store of intermediate state that grows linearly with sequence length. Efficient management of this memory is critical for throughput. While \u003cstrong\u003ePagedAttention\u003c\/strong\u003e (popularized by vLLM) became the industry standard by solving memory fragmentation via software, recent research suggests that leveraging the GPU’s native hardware Memory Management Unit (MMU) offers a more performant and portable solution.\u003c\/p\u003e\n\u003ch4 id=\u0022the-status-quo-pagedattention-and-software-tables\u0022\u003e\n The Status Quo: PagedAttention and Software Tables\n \u003ca class=\u0022heading-link\u0022 href=\u0022#the-status-quo-pagedattention-and-software-tables\u0022\u003e\n \u003ci class=\u0022fa-solid fa-link\u0022 aria-hidden=\u0022true\u0022 title=\u0022Link to heading\u0022\u003e\u003c\/i\u003e\n \u003cspan class=\u0022sr-only\u0022\u003eLink to heading\u003c\/span\u003e\n \u003c\/a\u003e\n\u003c\/h4\u003e\n\u003cp\u003ePrior to PagedAttention, systems allocated contiguous memory for the maximum possible context length, leading to severe fragmentation and wasted memory. PagedAttention addressed this by chunking the KV cache into non-contiguous blocks, managed by a software-defined \u0026ldquo;page table\u0026rdquo; (the Block Table) [1].\u003c\/p\u003e","author":{"@type":"Person","name":"Eric X. Liu"}}</script></head><body class="preload-transitions colorscheme-auto"><div class=float-container><a id=dark-mode-toggle class=colorscheme-toggle><i class="fa-solid fa-adjust fa-fw" aria-hidden=true></i></a></div><main class=wrapper><nav class=navigation><section class=container><a class=navigation-title href=https://ericxliu.me/>Eric X. Liu's Personal Page
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<time datetime=2025-12-08T00:00:00Z>December 8, 2025
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4-minute read</span></div></div></header><div class=post-content><p>Large Language Model (LLM) inference is memory-bound, primarily due to the Key-Value (KV) cache—a store of intermediate state that grows linearly with sequence length. Efficient management of this memory is critical for throughput. While <strong>PagedAttention</strong> (popularized by vLLM) became the industry standard by solving memory fragmentation via software, recent research suggests that leveraging the GPU’s native hardware Memory Management Unit (MMU) offers a more performant and portable solution.</p><h4 id=the-status-quo-pagedattention-and-software-tables>The Status Quo: PagedAttention and Software Tables
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@@ -31,4 +31,4 @@ The GPU TLB hierarchy is sensitive to page sizes.</p><ul><li><strong>4KB Pages:<
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2016 -
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2016 -
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2025
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