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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. 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<!doctype html><html lang=en><head><title>About · 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="About"><meta name=twitter:description content="Eric X. Liu - Software & Performance Engineer at Google. 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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="article"><meta property="article:published_time" content="2018-06-01T07:13:52+00:00"><meta property="article:modified_time" content="2020-06-16T23:30:17-07:00"><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. 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posts/benchmarking-llms-on-jetson-orin-nano/index.html
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NVIDIA&rsquo;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&rsquo;s a catch—one that reveals a fundamental tension in modern edge AI hardware design. NVIDIA&rsquo;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&rsquo;s a catch—one that reveals a fundamental tension in modern edge AI hardware design.
After running 66 inference tests across seven different language models ranging from 0.5B to 5.4B parameters, I discovered something counterintuitive: the device&rsquo;s computational muscle sits largely idle during single-stream LLM inference. The bottleneck isn&rsquo;t computation—it&rsquo;s memory bandwidth. This isn&rsquo;t just a quirk of one device; it&rsquo;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 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="Why Your Jetson Orin Nano's 40 TOPS Goes Unused (And What That Means for Edge AI)"><meta name=twitter: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. After running 66 inference tests across seven different language models ranging from 0.5B to 5.4B parameters, I discovered something counterintuitive: the device&rsquo;s computational muscle sits largely idle during single-stream LLM inference. The bottleneck isn&rsquo;t computation—it&rsquo;s memory bandwidth. This isn&rsquo;t just a quirk of one device; it&rsquo;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 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="Why Your Jetson Orin Nano's 40 TOPS Goes Unused (And What That Means for Edge AI)"><meta name=twitter: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.
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. 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.
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=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 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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<time datetime=2025-10-04T00:00:00Z>October 4, 2025 <time datetime=2025-10-04T00:00:00Z>October 4, 2025
@@ -62,4 +62,4 @@ After running 66 inference tests across seven different language models ranging
2016 - 2016 -
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posts/breville-barista-pro-maintenance/index.html
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@@ -8,7 +8,7 @@
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 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="Breville Barista Pro Maintenance"><meta name=twitter: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. 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 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="Breville Barista Pro Maintenance"><meta name=twitter: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.
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. 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.
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=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 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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<time datetime=2025-08-16T00:00:00Z>August 16, 2025 <time datetime=2025-08-16T00:00:00Z>August 16, 2025
@@ -25,4 +25,4 @@ Understanding the Two Primary Maintenance Cycles Link to heading The Breville Ba
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<!doctype html><html lang=en><head><title>Mastering Your Breville Barista Pro: The Ultimate Guide to Dialing In Espresso · 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="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. <!doctype html><html lang=en><head><title>Mastering Your Breville Barista Pro: The Ultimate Guide to Dialing In Espresso · 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="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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<time datetime=2025-08-07T00:00:00Z>August 7, 2025 <time datetime=2025-08-07T00:00:00Z>August 7, 2025
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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> <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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The Problem: The Problem:
Many routing mechanisms, especially &ldquo;Top-K routing,&rdquo; 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 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="Mixture-of-Experts (MoE) Models Challenges & Solutions in Practice"><meta name=twitter: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. Many routing mechanisms, especially &ldquo;Top-K routing,&rdquo; 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 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="Mixture-of-Experts (MoE) Models Challenges & Solutions in Practice"><meta name=twitter: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.
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. 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.
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=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 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 <time datetime=2025-07-02T00:00:00Z>July 2, 2025
@@ -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/supabase-deep-dive/>Supabase Deep Dive: It's Not Magic, It's Just Postgres</a></li><li><span class=date>August 2, 2025</span> <a class=title href=/posts/supabase-deep-dive/>Supabase Deep Dive: It's Not Magic, It's Just Postgres</a></li><li><span class=date>August 2, 2025</span>
@@ -11,4 +11,4 @@
2016 - 2016 -
2025 2025
Eric X. Liu Eric X. Liu
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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. 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 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). "><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).
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). 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).
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=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 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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<time datetime=2025-08-02T00:00:00Z>August 2, 2025 <time datetime=2025-08-02T00:00:00Z>August 2, 2025
@@ -25,4 +25,4 @@ where <code>δ_t = r_t + γV(s_{t+1}) - V(s_t)</code></p><ul><li><strong>γ (gam
2016 - 2016 -
2025 2025
Eric X. Liu Eric X. Liu
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@@ -5,7 +5,7 @@ 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 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="Fixing GPU Operator Pods Stuck in Init: Secure Boot, DKMS, and MOK on Proxmox + Debian"><meta name=twitter: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: That message is the tell: Secure Boot is enabled and the kernel refuses to load modules not signed by a trusted key."><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="Fixing GPU Operator Pods Stuck in Init: Secure Boot, DKMS, and MOK on Proxmox + Debian"><meta name=twitter: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:
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: 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:
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=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 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. 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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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<time datetime=2025-08-09T00:00:00Z>August 9, 2025 <time datetime=2025-08-09T00:00:00Z>August 9, 2025
@@ -59,4 +59,4 @@ nvidia-smi failed to communicate with the NVIDIA driver modprobe nvidia → “K
2016 - 2016 -
2025 2025
Eric X. Liu Eric X. Liu
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posts/supabase-deep-dive/index.html
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@@ -1,7 +1,7 @@
<!doctype html><html lang=en><head><title>Supabase Deep Dive: It's Not Magic, It's Just Postgres · 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="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. <!doctype html><html lang=en><head><title>Supabase Deep Dive: It's Not Magic, It's Just Postgres · 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="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.
Supabase enters this space with a radically different philosophy: transparency. 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."><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="Supabase Deep Dive: It's Not Magic, It's Just Postgres"><meta name=twitter: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. Supabase enters this space with a radically different philosophy: transparency. 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."><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="Supabase Deep Dive: It's Not Magic, It's Just Postgres"><meta name=twitter: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.
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. 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.
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=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 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. 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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. 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<time datetime=2025-08-03T00:00:00Z>August 3, 2025 <time datetime=2025-08-03T00:00:00Z>August 3, 2025
@@ -90,4 +90,4 @@ Supabase enters this space with a radically different philosophy: transparency.
2016 - 2016 -
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<!doctype html><html lang=en><head><title>An Architectural Deep Dive of T5 · 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="In the rapidly evolving landscape of Large Language Models, a few key architectures define the dominant paradigms. Today, the &ldquo;decoder-only&rdquo; 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. <!doctype html><html lang=en><head><title>An Architectural Deep Dive of T5 · 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="In the rapidly evolving landscape of Large Language Models, a few key architectures define the dominant paradigms. Today, the &ldquo;decoder-only&rdquo; 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.
But to truly understand the field, we must look at the pivotal models that explored different paths. Google&rsquo;s T5, or Text-to-Text Transfer Transformer, stands out as one of the most influential. It didn&rsquo;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 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="An Architectural Deep Dive of T5"><meta name=twitter: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. But to truly understand the field, we must look at the pivotal models that explored different paths. Google&rsquo;s T5, or Text-to-Text Transfer Transformer, stands out as one of the most influential. It didn&rsquo;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 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="An Architectural Deep Dive of T5"><meta name=twitter: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.
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. 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.
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=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. 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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 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. 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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. 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<time datetime=2025-06-01T00:00:00Z>June 1, 2025 <time datetime=2025-06-01T00:00:00Z>June 1, 2025
@@ -30,4 +30,4 @@ But to truly understand the field, we must look at the pivotal models that explo
2016 - 2016 -
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<!doctype html><html lang=en><head><title>The Convergence of Fast Weights, Linear Attention, and State Space Models · 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="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 (&ldquo;Fast Weights&rdquo;) and efficient engineering solutions like Linear Transformers and State Space Models (SSMs). <!doctype html><html lang=en><head><title>The Convergence of Fast Weights, Linear Attention, and State Space Models · 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="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 (&ldquo;Fast Weights&rdquo;) and efficient engineering solutions like Linear Transformers and State Space Models (SSMs).
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 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="The Convergence of Fast Weights, Linear Attention, and State Space Models"><meta name=twitter: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). 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 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="The Convergence of Fast Weights, Linear Attention, and State Space Models"><meta name=twitter: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).
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). 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).
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=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. 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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. 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<time datetime=2025-12-19T00:00:00Z>December 19, 2025 <time datetime=2025-12-19T00:00:00Z>December 19, 2025
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posts/transformer-s-core-mechanics/index.html
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In deep learning, a &ldquo;channel&rdquo; 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&rsquo;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. In deep learning, a &ldquo;channel&rdquo; 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&rsquo;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.
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. 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.
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=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 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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<time datetime=2025-04-01T00:00:00Z>April 1, 2025 <time datetime=2025-04-01T00:00:00Z>April 1, 2025
@@ -36,4 +36,4 @@ In deep learning, a &ldquo;channel&rdquo; can be thought of as a feature dimensi
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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. <!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.
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. 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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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 This article documents that journey. 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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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<time datetime=2025-09-22T00:00:00Z>September 22, 2025 <time datetime=2025-09-22T00:00:00Z>September 22, 2025
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Eric X. Liu Eric X. Liu
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<time datetime=2020-10-26T04:14:43Z>October 26, 2020 <time datetime=2020-10-26T04:14:43Z>October 26, 2020
@@ -9,4 +9,4 @@ One-minute read</span></div></div></header><div class=post-content><ul><li><a hr
2016 - 2016 -
2025 2025
Eric X. Liu Eric X. Liu
<a href="https://git.ericxliu.me/eric/ericxliu-me/commit/4c1d048">[4c1d048]</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> <a href="https://git.ericxliu.me/eric/ericxliu-me/commit/f976130">[f976130]</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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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 &ldquo;page table&rdquo; (the Block Table) [1]."><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="vAttention"><meta name=twitter: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. 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 &ldquo;page table&rdquo; (the Block Table) [1]."><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="vAttention"><meta name=twitter: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.
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. 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.
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=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 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 <time datetime=2025-12-08T00:00:00Z>December 8, 2025
@@ -31,4 +31,4 @@ The GPU TLB hierarchy is sensitive to page sizes.</p><ul><li><strong>4KB Pages:<
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