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<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Eric X. Liu's Personal Page</title><link>/</link><description>Recent content on Eric X. Liu's Personal Page</description><generator>Hugo</generator><language>en</language><lastBuildDate>Thu, 02 Oct 2025 08:36:58 +0000</lastBuildDate><atom:link href="/index.xml" rel="self" type="application/rss+xml"/><item><title>Flashing Jetson Orin Nano in Virtualized Environments</title><link>/posts/flashing-jetson-orin-nano-in-virtualized-environments/</link><pubDate>Thu, 02 Oct 2025 00:00:00 +0000</pubDate><guid>/posts/flashing-jetson-orin-nano-in-virtualized-environments/</guid><description><h1 id="flashing-jetson-orin-nano-in-virtualized-environments">
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<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Eric X. Liu's Personal Page</title><link>/</link><description>Recent content on Eric X. Liu's Personal Page</description><generator>Hugo</generator><language>en</language><lastBuildDate>Thu, 02 Oct 2025 08:42:39 +0000</lastBuildDate><atom:link href="/index.xml" rel="self" type="application/rss+xml"/><item><title>Flashing Jetson Orin Nano in Virtualized Environments</title><link>/posts/flashing-jetson-orin-nano-in-virtualized-environments/</link><pubDate>Thu, 02 Oct 2025 00:00:00 +0000</pubDate><guid>/posts/flashing-jetson-orin-nano-in-virtualized-environments/</guid><description><h1 id="flashing-jetson-orin-nano-in-virtualized-environments">
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Flashing Jetson Orin Nano in Virtualized Environments
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<a class="heading-link" href="#flashing-jetson-orin-nano-in-virtualized-environments">
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<i class="fa-solid fa-link" aria-hidden="true" title="Link to heading"></i>
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<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></description></item><item><title>Supabase Deep Dive: It's Not Magic, It's Just Postgres</title><link>/posts/supabase-deep-dive/</link><pubDate>Sun, 03 Aug 2025 00:00:00 +0000</pubDate><guid>/posts/supabase-deep-dive/</guid><description><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&rsquo;s really going on.</p>
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<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&rsquo;s most trusted relational database: PostgreSQL. The &ldquo;magic&rdquo; isn&rsquo;t a proprietary black box; it&rsquo;s a carefully assembled suite of open-source tools that enhance Postgres, not hide it.</p></description></item><item><title>A Deep Dive into PPO for Language Models</title><link>/posts/ppo-for-language-models/</link><pubDate>Sat, 02 Aug 2025 00:00:00 +0000</pubDate><guid>/posts/ppo-for-language-models/</guid><description><p>Large Language Models (LLMs) have demonstrated astonishing capabilities, but out-of-the-box, they are simply powerful text predictors. They don&rsquo;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>
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<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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<img src="http://localhost:4998/attachments/image-3632d923eed983f171fba4341825273101f1fc94.png?client=default&amp;bucket=obsidian" alt="S3 File"></p></description></item><item><title>Mixture-of-Experts (MoE) Models Challenges & Solutions in Practice</title><link>/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/</link><pubDate>Wed, 02 Jul 2025 00:00:00 +0000</pubDate><guid>/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/</guid><description><p>Mixture-of-Experts (MoEs) are neural network architectures that allow different parts of the model (called &ldquo;experts&rdquo;) to specialize in different types of inputs. A &ldquo;gating network&rdquo; or &ldquo;router&rdquo; learns to dispatch each input (or &ldquo;token&rdquo;) to a subset of these experts. While powerful for scaling models, MoEs introduce several practical challenges.</p>
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<img src="/images/ppo-for-language-models/7713bd3ecf27442e939b9190fa08165d.png" alt="S3 File"></p></description></item><item><title>Mixture-of-Experts (MoE) Models Challenges & Solutions in Practice</title><link>/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/</link><pubDate>Wed, 02 Jul 2025 00:00:00 +0000</pubDate><guid>/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/</guid><description><p>Mixture-of-Experts (MoEs) are neural network architectures that allow different parts of the model (called &ldquo;experts&rdquo;) to specialize in different types of inputs. A &ldquo;gating network&rdquo; or &ldquo;router&rdquo; learns to dispatch each input (or &ldquo;token&rdquo;) to a subset of these experts. While powerful for scaling models, MoEs introduce several practical challenges.</p>
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<h3 id="1-challenge-non-differentiability-of-routing-functions">
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1. Challenge: Non-Differentiability of Routing Functions
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<a class="heading-link" href="#1-challenge-non-differentiability-of-routing-functions">
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@@ -25,4 +25,4 @@ Understanding the Two Primary Maintenance Cycles Link to heading The Breville Ba
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Eric X. Liu
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<a href="https://git.ericxliu.me/eric/ericxliu-me/commit/ca87382">[ca87382]</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/ca87382">[ca87382]</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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Link to heading
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Flashing NVIDIA Jetson devices remotely presents unique challenges when the host machine is virtualized. This article documents the technical challenges, failures, and eventual success of flashing a Jetson Orin Nano Super developer kit using NVIDIA SDK Manager in various virtualized environments, specifically focusing on QEMU/KVM virtual machines and LXC containers on Proxmox VE."><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="Flashing Jetson Orin Nano in Virtualized Environments"><meta name=twitter:description content="Flashing Jetson Orin Nano in Virtualized Environments Link to heading Introduction Link to heading Flashing NVIDIA Jetson devices remotely presents unique challenges when the host machine is virtualized. This article documents the technical challenges, failures, and eventual success of flashing a Jetson Orin Nano Super developer kit using NVIDIA SDK Manager in various virtualized environments, specifically focusing on QEMU/KVM virtual machines and LXC containers on Proxmox VE."><meta property="og:url" content="/posts/flashing-jetson-orin-nano-in-virtualized-environments/"><meta property="og:site_name" content="Eric X. Liu's Personal Page"><meta property="og:title" content="Flashing Jetson Orin Nano in Virtualized Environments"><meta property="og:description" content="Flashing Jetson Orin Nano in Virtualized Environments Link to heading Introduction Link to heading Flashing NVIDIA Jetson devices remotely presents unique challenges when the host machine is virtualized. This article documents the technical challenges, failures, and eventual success of flashing a Jetson Orin Nano Super developer kit using NVIDIA SDK Manager in various virtualized environments, specifically focusing on QEMU/KVM virtual machines and LXC containers on Proxmox VE."><meta property="og:locale" content="en"><meta property="og:type" content="article"><meta property="article:section" content="posts"><meta property="article:published_time" content="2025-10-02T00:00:00+00:00"><meta property="article:modified_time" content="2025-10-02T08:36:58+00:00"><link rel=canonical href=/posts/flashing-jetson-orin-nano-in-virtualized-environments/><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.c8e4eea149ae1dc7c61ba9b0781793711a4e657f7e07a4413f9abc46d52dffc4.css integrity="sha256-yOTuoUmuHcfGG6mweBeTcRpOZX9+B6RBP5q8RtUt/8Q=" 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></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=/>Eric X. Liu's Personal Page
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Flashing NVIDIA Jetson devices remotely presents unique challenges when the host machine is virtualized. This article documents the technical challenges, failures, and eventual success of flashing a Jetson Orin Nano Super developer kit using NVIDIA SDK Manager in various virtualized environments, specifically focusing on QEMU/KVM virtual machines and LXC containers on Proxmox VE."><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="Flashing Jetson Orin Nano in Virtualized Environments"><meta name=twitter:description content="Flashing Jetson Orin Nano in Virtualized Environments Link to heading Introduction Link to heading Flashing NVIDIA Jetson devices remotely presents unique challenges when the host machine is virtualized. This article documents the technical challenges, failures, and eventual success of flashing a Jetson Orin Nano Super developer kit using NVIDIA SDK Manager in various virtualized environments, specifically focusing on QEMU/KVM virtual machines and LXC containers on Proxmox VE."><meta property="og:url" content="/posts/flashing-jetson-orin-nano-in-virtualized-environments/"><meta property="og:site_name" content="Eric X. Liu's Personal Page"><meta property="og:title" content="Flashing Jetson Orin Nano in Virtualized Environments"><meta property="og:description" content="Flashing Jetson Orin Nano in Virtualized Environments Link to heading Introduction Link to heading Flashing NVIDIA Jetson devices remotely presents unique challenges when the host machine is virtualized. This article documents the technical challenges, failures, and eventual success of flashing a Jetson Orin Nano Super developer kit using NVIDIA SDK Manager in various virtualized environments, specifically focusing on QEMU/KVM virtual machines and LXC containers on Proxmox VE."><meta property="og:locale" content="en"><meta property="og:type" content="article"><meta property="article:section" content="posts"><meta property="article:published_time" content="2025-10-02T00:00:00+00:00"><meta property="article:modified_time" content="2025-10-02T08:42:39+00:00"><link rel=canonical href=/posts/flashing-jetson-orin-nano-in-virtualized-environments/><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.c8e4eea149ae1dc7c61ba9b0781793711a4e657f7e07a4413f9abc46d52dffc4.css integrity="sha256-yOTuoUmuHcfGG6mweBeTcRpOZX9+B6RBP5q8RtUt/8Q=" 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></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=/>Eric X. Liu's Personal Page
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</a><input type=checkbox id=menu-toggle>
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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=/posts/flashing-jetson-orin-nano-in-virtualized-environments/>Flashing Jetson Orin Nano in Virtualized Environments</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=2025-10-02T00:00:00Z>October 2, 2025
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<a class=heading-link href=#flashing-jetson-orin-nano-in-virtualized-environments><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
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<span class=sr-only>Link to heading</span></a></h1><h2 id=introduction>Introduction
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<span class=sr-only>Link to heading</span></a></h2><p>Flashing NVIDIA Jetson devices remotely presents unique challenges when the host machine is virtualized. This article documents the technical challenges, failures, and eventual success of flashing a Jetson Orin Nano Super developer kit using NVIDIA SDK Manager in various virtualized environments, specifically focusing on QEMU/KVM virtual machines and LXC containers on Proxmox VE.</p><p><img src="http://localhost:4998/attachments/image-7c88938eaa4db1b7eafc437b9067b8790998fc71.png?client=default&bucket=obsidian" alt="S3 File"></p><h3 id=the-constraint-hypervisor-only-infrastructure>The Constraint: Hypervisor-Only Infrastructure
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<span class=sr-only>Link to heading</span></a></h2><p>Flashing NVIDIA Jetson devices remotely presents unique challenges when the host machine is virtualized. This article documents the technical challenges, failures, and eventual success of flashing a Jetson Orin Nano Super developer kit using NVIDIA SDK Manager in various virtualized environments, specifically focusing on QEMU/KVM virtual machines and LXC containers on Proxmox VE.</p><p><img src=/images/flashing-jetson-orin-nano-in-virtualized-environments/2803b917b5794452870bc8a0aa896381.png alt="S3 File"></p><h3 id=the-constraint-hypervisor-only-infrastructure>The Constraint: Hypervisor-Only Infrastructure
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<span class=sr-only>Link to heading</span></a></h3><p>This project operated under a specific constraint: the only available x86_64 machines were homelab servers running Proxmox VE as bare-metal hypervisors. There was no x86 laptop available, and the primary workstation was an Apple M4 Mac (ARM64 architecture incompatible with SDK Manager).</p><p>Installing SDK Manager directly on the Proxmox host OS was explicitly ruled out for several reasons:</p><ol><li><p><strong>Hypervisor Stability</strong>: The Proxmox hosts run critical infrastructure (Kubernetes clusters, Ceph storage, network services). Installing development tools and potentially conflicting dependencies directly on the hypervisor risks system stability.</p></li><li><p><strong>Dependency Conflicts</strong>: SDK Manager requires numerous dependencies (QEMU, specific Python versions, USB libraries) that could conflict with Proxmox’s carefully managed package versions.</p></li><li><p><strong>Clean Separation</strong>: Best practices dictate keeping hypervisor hosts minimal, with all workloads running in VMs or containers. This separation simplifies maintenance, updates, and disaster recovery.</p></li><li><p><strong>Repeatability</strong>: A solution confined to a VM or container can be easily replicated, backed up, and destroyed without affecting the host system.</p></li></ol><p>This constraint made the flashing process significantly more complex, as it required finding a virtualization method that could reliably handle the Jetson’s USB communication requirements without installing anything on the Proxmox host beyond standard virtualization features.</p><h2 id=background-jetson-flashing-requirements>Background: Jetson Flashing Requirements
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@@ -168,4 +168,4 @@ Flashing NVIDIA Jetson devices remotely presents unique challenges when the host
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<a href="https://git.ericxliu.me/eric/ericxliu-me/commit/ca87382">[ca87382]</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/ca87382">[ca87382]</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/ca87382">[ca87382]</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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<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Posts on Eric X. Liu's Personal Page</title><link>/posts/</link><description>Recent content in Posts on Eric X. Liu's Personal Page</description><generator>Hugo</generator><language>en</language><lastBuildDate>Thu, 02 Oct 2025 08:36:58 +0000</lastBuildDate><atom:link href="/posts/index.xml" rel="self" type="application/rss+xml"/><item><title>Flashing Jetson Orin Nano in Virtualized Environments</title><link>/posts/flashing-jetson-orin-nano-in-virtualized-environments/</link><pubDate>Thu, 02 Oct 2025 00:00:00 +0000</pubDate><guid>/posts/flashing-jetson-orin-nano-in-virtualized-environments/</guid><description><h1 id="flashing-jetson-orin-nano-in-virtualized-environments">
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<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Posts on Eric X. Liu's Personal Page</title><link>/posts/</link><description>Recent content in Posts on Eric X. Liu's Personal Page</description><generator>Hugo</generator><language>en</language><lastBuildDate>Thu, 02 Oct 2025 08:42:39 +0000</lastBuildDate><atom:link href="/posts/index.xml" rel="self" type="application/rss+xml"/><item><title>Flashing Jetson Orin Nano in Virtualized Environments</title><link>/posts/flashing-jetson-orin-nano-in-virtualized-environments/</link><pubDate>Thu, 02 Oct 2025 00:00:00 +0000</pubDate><guid>/posts/flashing-jetson-orin-nano-in-virtualized-environments/</guid><description><h1 id="flashing-jetson-orin-nano-in-virtualized-environments">
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Flashing Jetson Orin Nano in Virtualized Environments
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<a class="heading-link" href="#flashing-jetson-orin-nano-in-virtualized-environments">
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<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></description></item><item><title>Supabase Deep Dive: It's Not Magic, It's Just Postgres</title><link>/posts/supabase-deep-dive/</link><pubDate>Sun, 03 Aug 2025 00:00:00 +0000</pubDate><guid>/posts/supabase-deep-dive/</guid><description><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&rsquo;s really going on.</p>
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<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&rsquo;s most trusted relational database: PostgreSQL. The &ldquo;magic&rdquo; isn&rsquo;t a proprietary black box; it&rsquo;s a carefully assembled suite of open-source tools that enhance Postgres, not hide it.</p></description></item><item><title>A Deep Dive into PPO for Language Models</title><link>/posts/ppo-for-language-models/</link><pubDate>Sat, 02 Aug 2025 00:00:00 +0000</pubDate><guid>/posts/ppo-for-language-models/</guid><description><p>Large Language Models (LLMs) have demonstrated astonishing capabilities, but out-of-the-box, they are simply powerful text predictors. They don&rsquo;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>
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<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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<img src="http://localhost:4998/attachments/image-3632d923eed983f171fba4341825273101f1fc94.png?client=default&amp;bucket=obsidian" alt="S3 File"></p></description></item><item><title>Mixture-of-Experts (MoE) Models Challenges & Solutions in Practice</title><link>/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/</link><pubDate>Wed, 02 Jul 2025 00:00:00 +0000</pubDate><guid>/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/</guid><description><p>Mixture-of-Experts (MoEs) are neural network architectures that allow different parts of the model (called &ldquo;experts&rdquo;) to specialize in different types of inputs. A &ldquo;gating network&rdquo; or &ldquo;router&rdquo; learns to dispatch each input (or &ldquo;token&rdquo;) to a subset of these experts. While powerful for scaling models, MoEs introduce several practical challenges.</p>
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<img src="/images/ppo-for-language-models/7713bd3ecf27442e939b9190fa08165d.png" alt="S3 File"></p></description></item><item><title>Mixture-of-Experts (MoE) Models Challenges & Solutions in Practice</title><link>/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/</link><pubDate>Wed, 02 Jul 2025 00:00:00 +0000</pubDate><guid>/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/</guid><description><p>Mixture-of-Experts (MoEs) are neural network architectures that allow different parts of the model (called &ldquo;experts&rdquo;) to specialize in different types of inputs. A &ldquo;gating network&rdquo; or &ldquo;router&rdquo; learns to dispatch each input (or &ldquo;token&rdquo;) to a subset of these experts. While powerful for scaling models, MoEs introduce several practical challenges.</p>
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<h3 id="1-challenge-non-differentiability-of-routing-functions">
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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 href="https://git.ericxliu.me/eric/ericxliu-me/commit/ca87382">[ca87382]</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/7ef6ce1">[7ef6ce1]</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/ca87382">[ca87382]</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/ca87382">[ca87382]</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/7ef6ce1">[7ef6ce1]</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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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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"><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="A Deep Dive into PPO for Language Models"><meta name=twitter: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:url" content="/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="2025-10-02T07:22:54+00:00"><link rel=canonical href=/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.c8e4eea149ae1dc7c61ba9b0781793711a4e657f7e07a4413f9abc46d52dffc4.css integrity="sha256-yOTuoUmuHcfGG6mweBeTcRpOZX9+B6RBP5q8RtUt/8Q=" 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></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=/>Eric X. Liu's Personal Page
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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=canonical href=/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.c8e4eea149ae1dc7c61ba9b0781793711a4e657f7e07a4413f9abc46d52dffc4.css integrity="sha256-yOTuoUmuHcfGG6mweBeTcRpOZX9+B6RBP5q8RtUt/8Q=" 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></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=/>Eric X. Liu's Personal Page
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</a><input type=checkbox id=menu-toggle>
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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=/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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<img src="http://localhost:4998/attachments/image-3632d923eed983f171fba4341825273101f1fc94.png?client=default&bucket=obsidian" alt="S3 File"></p><p>This post will decode that diagram, piece by piece. We’ll explore the “why” behind each component, moving from high-level concepts to the deep technical reasoning that makes this process work.</p><h3 id=translating-rl-to-a-conversation>Translating RL to a Conversation
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<img src=/images/ppo-for-language-models/7713bd3ecf27442e939b9190fa08165d.png alt="S3 File"></p><p>This post will decode that diagram, piece by piece. We’ll explore the “why” behind each component, moving from high-level concepts to the deep technical reasoning that makes this process work.</p><h3 id=translating-rl-to-a-conversation>Translating RL to a Conversation
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<span class=sr-only>Link to heading</span></a></h3><p>The first step is to understand how the traditional language of reinforcement learning maps to the world of text generation.</p><ul><li><strong>State (<code>s_t</code>)</strong>: In a chat setting, the “state” is the context of the conversation so far. It’s the initial prompt (<code>x</code>) plus all the text the model has generated up to the current moment (<code>y₁, ..., y_{t-1}</code>).</li><li><strong>Action (<code>a_t</code>)</strong>: The “action” is the model’s decision at each step. For an LLM, this means generating the very next token (<code>y_t</code>). A full response is a sequence of these actions.blob:https://aistudio.google.com/872e746f-88c1-40ec-8e45-fa0efce97299</li><li><strong>Reward (<code>r</code>)</strong>: The “reward” is a numeric score that tells the model how good its full response (<code>y</code>) was. This score comes from a separate <strong>Reward Model</strong>, which has been trained on a large dataset of human preference comparisons (e.g., humans rating which of two responses is better). This reward is often only awarded at the end of the entire generated sequence.</li></ul><p>Let’s make this concrete. If a user provides the prompt <strong>(x)</strong>: <em>“The best thing about AI is”</em>, and the model generates the response <strong>(y)</strong>: <em>“its potential to solve problems.”</em>, here is how it’s broken down for training:</p><ul><li><strong>State 1</strong>: “The best thing about AI is”<ul><li><strong>Action 1</strong>: “its”</li></ul></li><li><strong>State 2</strong>: “The best thing about AI is its”<ul><li><strong>Action 2</strong>: " potential"</li></ul></li><li><strong>State 3</strong>: “The best thing about AI is its potential”<ul><li><strong>Action 3</strong>: " to"</li></ul></li><li>…and so on for every generated token.</li></ul><p>This breakdown transforms a single prompt-response pair into a rich trajectory of state-action pairs, which becomes the raw data for our learning algorithm.</p><h3 id=the-cast-of-models-an-actor-critic-ensemble>The Cast of Models: An Actor-Critic Ensemble
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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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<a href="https://git.ericxliu.me/eric/ericxliu-me/commit/ca87382">[ca87382]</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/ca87382">[ca87382]</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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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 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="Transformer's Core Mechanics"><meta name=twitter: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:url" content="/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="2025-10-02T07:22:54+00:00"><link rel=canonical href=/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.c8e4eea149ae1dc7c61ba9b0781793711a4e657f7e07a4413f9abc46d52dffc4.css integrity="sha256-yOTuoUmuHcfGG6mweBeTcRpOZX9+B6RBP5q8RtUt/8Q=" 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></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=/>Eric X. Liu's Personal Page
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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=canonical href=/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.c8e4eea149ae1dc7c61ba9b0781793711a4e657f7e07a4413f9abc46d52dffc4.css integrity="sha256-yOTuoUmuHcfGG6mweBeTcRpOZX9+B6RBP5q8RtUt/8Q=" 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></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=/>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=/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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<span class=sr-only>Link to heading</span></a></h3><p>A Transformer layer is primarily composed of two sub-layers: a Multi-Head Attention block and a position-wise Feed-Forward Network (FFN). The parameters for these are stored in several key weight matrices. Understanding their dimensions is crucial.</p><p>Let’s define our variables:</p><ul><li><code>d_model</code>: The core embedding dimension.</li><li><code>d_ff</code>: The inner dimension of the FFN, typically <code>4 * d_model</code>.</li><li><code>h</code>: The number of attention heads.</li><li><code>d_head</code>: The dimension of each attention head, where <code>d_model = h * d_head</code>.</li></ul><p>The dimensions of the weight matrices are as follows:</p><table><thead><tr><th>Layer</th><th>Weight Matrix</th><th>Input Vector Shape</th><th>Output Vector Shape</th><th><strong>Weight Matrix Dimension</strong></th></tr></thead><tbody><tr><td><strong>Attention Projections</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Query</td><td><code>W_Q</code></td><td><code>d_model</code></td><td><code>d_model</code></td><td><strong><code>[d_model, d_model]</code></strong></td></tr><tr><td>Key</td><td><code>W_K</code></td><td><code>d_model</code></td><td><code>d_model</code></td><td><strong><code>[d_model, d_model]</code></strong></td></tr><tr><td>Value</td><td><code>W_V</code></td><td><code>d_model</code></td><td><code>d_model</code></td><td><strong><code>[d_model, d_model]</code></strong></td></tr><tr><td>Output</td><td><code>W_O</code></td><td><code>d_model</code></td><td><code>d_model</code></td><td><strong><code>[d_model, d_model]</code></strong></td></tr><tr><td><strong>Feed-Forward Network</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Layer 1 (Up-projection)</td><td><code>W_ff1</code></td><td><code>d_model</code></td><td><code>d_ff</code></td><td><strong><code>[d_model, d_ff]</code></strong></td></tr><tr><td>Layer 2 (Down-projection)</td><td><code>W_ff2</code></td><td><code>d_ff</code></td><td><code>d_model</code></td><td><strong><code>[d_ff, d_model]</code></strong></td></tr></tbody></table><h3 id=3-deconstructing-multi-head-attention-mha>3. Deconstructing Multi-Head Attention (MHA)
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<span class=sr-only>Link to heading</span></a></h3><p>The core innovation of the Transformer is Multi-Head Attention. It allows the model to weigh the importance of different tokens in the sequence from multiple perspectives simultaneously.
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<img src="http://localhost:4998/attachments/image-c64b0f9df1e4981c4ecdb3b60e8bc78c426ffa68.png?client=default&bucket=obsidian" alt="S3 File"></p><h4 id=31-the-why-beyond-a-single-attention>3.1. The “Why”: Beyond a Single Attention
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<span class=sr-only>Link to heading</span></a></h4><p>A single attention mechanism would force the model to average all types of linguistic relationships into one pattern. MHA avoids this by creating <code>h</code> parallel subspaces. Each “head” can specialize, with one head learning syntactic dependencies, another tracking semantic similarity, and so on. This creates a much richer representation.</p><h4 id=32-an-encodingdecoding-analogy>3.2. An Encoding/Decoding Analogy
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<!doctype html><html lang=en><head><title>UniFi VLAN Migration to Zone-Based Architecture · 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="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 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="UniFi VLAN Migration to Zone-Based Architecture"><meta name=twitter: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:url" content="/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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<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=/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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<span class=sr-only>Link to heading</span></a></h3><p>The culmination of these lessons is a network architecture defined by clear, logical zones, each with a distinct purpose and trust level. This model simplifies firewall management and provides a robust security posture that is easy to understand at a glance.</p><h4 id=network-zones-and-their-roles>Network Zones and Their Roles
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<span class=sr-only>Link to heading</span></a></h4><p>The final configuration groups the individual VLANs into distinct zones, forming the foundation of the security policy.</p><ul><li><strong>Internal:</strong> Contains the <code>corp</code> network. This is the most trusted zone for daily work.</li><li><strong>DMZ:</strong> Contains the <code>dns</code> and <code>prod</code> networks for semi-trusted, exposed services.</li><li><strong>IoT:</strong> Contains the <code>iot</code> network. This is a low-trust zone for smart devices.</li><li><strong>Management:</strong> Contains the <code>management</code> network. This is a highly privileged, isolated zone for network infrastructure.
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<img src="http://localhost:4998/attachments/image-167d5cef9e79e622fff779f3671492a8a5a343ea.png?client=default&bucket=obsidian" alt="S3 File"></li></ul><h4 id=the-security-policy-matrix>The Security Policy Matrix
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<img src=/images/unifi-vlan-migration-to-zone-based-architecture/472bf0cd504f4cd7ab7a33cd3322a5f1.png alt="S3 File"></li></ul><h4 id=the-security-policy-matrix>The Security Policy Matrix
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<a class=heading-link href=#the-security-policy-matrix><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
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<span class=sr-only>Link to heading</span></a></h4><p>The true power of this model is realized in the firewall’s zone matrix, which dictates the default traffic flow between each zone.
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<img src="http://localhost:4998/attachments/image-4b9dbea5f7ceb0446d517305bc281b74e7f22ffc.png?client=default&bucket=obsidian" alt="S3 File"></p><p>This matrix enforces the desired security policy with clear, high-level rules:</p><ul><li><strong>Complete IoT Isolation:</strong> The <code>IoT</code> row shows that devices in this zone are blocked from initiating any communication with any other internal zone. Their only allowed path is out to the internet.</li><li><strong>Protected Management Plane:</strong> The <code>management</code> row and column are almost entirely red. The critical network infrastructure is blocked from initiating contact with any user-facing zone, and vice-versa, following the principle of least privilege.</li><li><strong>Controlled DMZ Access:</strong> The <code>DMZ</code> is prevented from initiating connections to the trusted <code>Internal</code> zone, preventing a compromised public-facing server from being used as a pivot point to attack internal devices.</li></ul><h4 id=granular-intra-zone-control>Granular Intra-Zone Control
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<img src=/images/unifi-vlan-migration-to-zone-based-architecture/663d732d14fc4fa8ad051c6926523efb.png alt="S3 File"></p><p>This matrix enforces the desired security policy with clear, high-level rules:</p><ul><li><strong>Complete IoT Isolation:</strong> The <code>IoT</code> row shows that devices in this zone are blocked from initiating any communication with any other internal zone. Their only allowed path is out to the internet.</li><li><strong>Protected Management Plane:</strong> The <code>management</code> row and column are almost entirely red. The critical network infrastructure is blocked from initiating contact with any user-facing zone, and vice-versa, following the principle of least privilege.</li><li><strong>Controlled DMZ Access:</strong> The <code>DMZ</code> is prevented from initiating connections to the trusted <code>Internal</code> zone, preventing a compromised public-facing server from being used as a pivot point to attack internal devices.</li></ul><h4 id=granular-intra-zone-control>Granular Intra-Zone Control
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<a class=heading-link href=#granular-intra-zone-control><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
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<span class=sr-only>Link to heading</span></a></h4><p>Beyond the high-level zone policies, the configuration also implements granular rules to control traffic <em>within</em> a single zone, providing defense-in-depth.</p><p>These rules explicitly define the communication paths between services. For instance, rules allow a specific device to access a Kubernetes load balancer while another rule allows general DNS access within the zone. This ensures that even within a semi-trusted zone, services can only communicate in expected and necessary ways, further reducing the potential attack surface.</p><p>By adhering to these principles, what began as a day of frustrating troubleshooting evolved into a robust, layered, and logically segmented network that balances simplicity with strong security practices.</p><hr><h3 id=references>References
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