{"id":520219,"date":"2025-10-18T17:30:32","date_gmt":"2025-10-19T00:30:32","guid":{"rendered":"https:\/\/jorgep.com\/blog\/?p=520219"},"modified":"2026-04-15T17:54:32","modified_gmt":"2026-04-16T00:54:32","slug":"understanding-llm-mixture-of-experts-moe","status":"publish","type":"post","link":"https:\/\/jorgep.com\/blog\/understanding-llm-mixture-of-experts-moe\/","title":{"rendered":"Understanding LLM Mixture of Experts (MoE)"},"content":{"rendered":"\n<div class=\"wp-block-columns has-theme-palette-7-background-color has-background is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p>Part of: <strong> <a href=\"https:\/\/jorgep.com\/blog\/series-ai-learnings\/\">AI Learning Series Here<\/a><\/strong><\/p>\n\n\n<style>.kadence-column395113_43ef2d-d5 > .kt-inside-inner-col,.kadence-column395113_43ef2d-d5 > .kt-inside-inner-col:before{border-top-left-radius:0px;border-top-right-radius:0px;border-bottom-right-radius:0px;border-bottom-left-radius:0px;}.kadence-column395113_43ef2d-d5 > .kt-inside-inner-col{column-gap:var(--global-kb-gap-sm, 1rem);}.kadence-column395113_43ef2d-d5 > .kt-inside-inner-col{flex-direction:column;}.kadence-column395113_43ef2d-d5 > .kt-inside-inner-col > .aligncenter{width:100%;}.kadence-column395113_43ef2d-d5 > .kt-inside-inner-col:before{opacity:0.3;}.kadence-column395113_43ef2d-d5{position:relative;}@media all and (max-width: 1024px){.kadence-column395113_43ef2d-d5 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}@media all and (max-width: 767px){.kadence-column395113_43ef2d-d5 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}<\/style>\n<div class=\"wp-block-kadence-column kadence-column395113_43ef2d-d5\"><div class=\"kt-inside-inner-col\"><style>.wp-block-kadence-advancedheading.kt-adv-heading510545_6813a5-28, .wp-block-kadence-advancedheading.kt-adv-heading510545_6813a5-28[data-kb-block=\"kb-adv-heading510545_6813a5-28\"]{font-size:var(--global-kb-font-size-sm, 0.9rem);font-style:normal;}.wp-block-kadence-advancedheading.kt-adv-heading510545_6813a5-28 mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading510545_6813a5-28[data-kb-block=\"kb-adv-heading510545_6813a5-28\"] mark.kt-highlight{font-style:normal;color:#f76a0c;-webkit-box-decoration-break:clone;box-decoration-break:clone;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.wp-block-kadence-advancedheading.kt-adv-heading510545_6813a5-28 img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading510545_6813a5-28[data-kb-block=\"kb-adv-heading510545_6813a5-28\"] img.kb-inline-image{width:150px;vertical-align:baseline;}<\/style>\n<p class=\"kt-adv-heading510545_6813a5-28 wp-block-kadence-advancedheading\" data-kb-block=\"kb-adv-heading510545_6813a5-28\">Quick Links:&nbsp;<a href=\"https:\/\/jorgep.com\/blog\/resources-for-learning-ai\/\">Resources for Learning AI<\/a> | <a href=\"https:\/\/jorgep.com\/blog\/keeping-up-with-ai\/\">Keep up with AI<\/a> | <a href=\"https:\/\/jorgep.com\/blog\/list-of-ai-tools\/\" data-type=\"post\" data-id=\"402818\">List of AI Tools<\/a><\/p>\n<\/div><\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><div class=\"wp-block-template-part\"><style>.wp-block-kadence-advancedheading.kt-adv-heading395113_c650df-47, .wp-block-kadence-advancedheading.kt-adv-heading395113_c650df-47[data-kb-block=\"kb-adv-heading395113_c650df-47\"]{text-align:center;font-size:var(--global-kb-font-size-md, 1.25rem);line-height:60px;font-style:normal;background-color:#f5a511;}.wp-block-kadence-advancedheading.kt-adv-heading395113_c650df-47 mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading395113_c650df-47[data-kb-block=\"kb-adv-heading395113_c650df-47\"] mark.kt-highlight{font-style:normal;color:#f76a0c;-webkit-box-decoration-break:clone;box-decoration-break:clone;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.wp-block-kadence-advancedheading.kt-adv-heading395113_c650df-47 img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading395113_c650df-47[data-kb-block=\"kb-adv-heading395113_c650df-47\"] img.kb-inline-image{width:150px;vertical-align:baseline;}<\/style>\n<p class=\"kt-adv-heading395113_c650df-47 wp-block-kadence-advancedheading\" data-kb-block=\"kb-adv-heading395113_c650df-47\">Subscribe to <a href=\"https:\/\/go.35s.be\/jtb\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>JorgeTechBits  newsletter<\/strong><\/a><\/p>\n<\/div><\/div>\n<\/div>\n\n\n\n<p><br><strong><em>To learn more about Local AI topics, check out <a href=\"https:\/\/jorgep.com\/blog\/local-ai-series\/\">related posts in the Lo<\/a><a href=\"https:\/\/jorgep.com\/blog\/local-ai-series\/\" target=\"_blank\" rel=\"noreferrer noopener\">cal AI Series<\/a>\u00a0<\/em><\/strong><\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n<style>.wp-block-kadence-advancedheading.kt-adv-heading519190_4a1b6f-84, .wp-block-kadence-advancedheading.kt-adv-heading519190_4a1b6f-84[data-kb-block=\"kb-adv-heading519190_4a1b6f-84\"]{font-size:var(--global-kb-font-size-sm, 0.9rem);font-style:normal;}.wp-block-kadence-advancedheading.kt-adv-heading519190_4a1b6f-84 mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading519190_4a1b6f-84[data-kb-block=\"kb-adv-heading519190_4a1b6f-84\"] mark.kt-highlight{font-style:normal;color:#f76a0c;-webkit-box-decoration-break:clone;box-decoration-break:clone;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.wp-block-kadence-advancedheading.kt-adv-heading519190_4a1b6f-84 img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading519190_4a1b6f-84[data-kb-block=\"kb-adv-heading519190_4a1b6f-84\"] img.kb-inline-image{width:150px;vertical-align:baseline;}<\/style>\n<p class=\"kt-adv-heading519190_4a1b6f-84 wp-block-kadence-advancedheading\" data-kb-block=\"kb-adv-heading519190_4a1b6f-84\">AI Disclaimer I love exploring new technology, and that includes using AI to help with research and editing! My digital &#8220;team&#8221; includes tools like Google Gemini, Notebook LM, Microsoft Copilot, Perplexity.ai, Claude.ai, and others as needed. They help me gather insights and polish content\u2014so you get the best, most up-to-date information possible.<\/p>\n\n\n\n<p>When you hear about the latest large language models (LLMs)\u2014like GPT-4, Claude, or Gemini\u2014it\u2019s easy to feel overwhelmed by their sheer scale. These models contain billions, sometimes trillions, of parameters.<\/p>\n\n\n\n<p>This incredible size is what gives them their broad capabilities\u2014from writing code and summarizing texts to answering complex questions and creating poetry. But that scale comes with a cost: massive computational requirements. Training or running such large models demands enormous processing power, making them slow and expensive to operate at scale.<\/p>\n\n\n\n<p>The AI industry needed a way to achieve the performance of huge models without the crushing computational cost. Enter the&nbsp;<strong>Mixture of Experts (MoE)<\/strong>&nbsp;architecture.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"what-is-mixture-of-experts-moe\">What Is Mixture of Experts (MoE)?<\/h2>\n\n\n\n<p>Mixture of Experts is an architectural approach that enables LLMs to be larger and more powerful while staying computationally efficient and fast.<\/p>\n\n\n\n<p>Think of a traditional model as a single, brilliant generalist who has to handle every question\u2014whether it\u2019s about physics or poetry\u2014using all their knowledge at once.<\/p>\n\n\n\n<p>By contrast, MoE is like assembling a team of specialized experts. The model is divided into smaller, specialized components called&nbsp;<strong>Experts<\/strong>. When a new question arrives, a control mechanism known as the&nbsp;<strong>Router<\/strong>&nbsp;determines which subset of experts (for example, two or four) are best suited to handle that specific input.<\/p>\n\n\n\n<p>Only those selected experts are activated, while the rest remain idle\u2014saving time and resources.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"the-university-panel-analogy\">The University Panel Analogy<\/h2>\n\n\n\n<p>Imagine you bring a complex consulting problem to two different firms:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Traditional LLM:<\/strong>\u00a0A single, massive firm analyzes your problem using every department\u2014finance, legal, science, and marketing\u2014whether relevant or not. It\u2019s thorough but slow and costly.<\/li>\n\n\n\n<li><strong>MoE Model:<\/strong>\u00a0A router reviews your issue and instantly decides it\u2019s a legal and finance problem. Only those departments are activated, collaborating efficiently while others stay inactive.<\/li>\n<\/ul>\n\n\n\n<p>This is how MoE achieves specialization and speed\u2014by activating only the expertise that matters.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"how-moe-works-the-technical-flow\">How MoE Works: The Technical Flow<\/h2>\n\n\n\n<p>The Mixture of Experts framework includes three key components:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>The Experts \u2014 The Knowledge Base<\/strong><br>Independent sub-networks trained for specific domains, such as code generation, reasoning, or conversational tone. Each expert specializes in a particular slice of knowledge.<\/li>\n\n\n\n<li><strong>The Router \u2014 The Gatekeeper<\/strong><br>A control layer that inspects incoming prompts. It determines which experts are most relevant based on the input\u2019s intent and content, then activates them selectively.<\/li>\n\n\n\n<li><strong>The Mixture \u2014 The Synthesis<\/strong><br>The selected experts process the input separately. Their outputs are then blended, or \u201cmixed,\u201d into a single unified response\u2014ensuring both depth and coherence.<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"why-mixture-of-experts-matters\">Why Mixture of Experts Matters<\/h2>\n\n\n\n<p>MoE isn\u2019t merely a clever efficiency hack\u2014it represents a structural breakthrough that transforms how large-scale AI operates. Its key benefits include:<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p>Because only a portion of the network activates at any given time, engineers can build\u00a0<strong>much larger models overall<\/strong>\u00a0without proportional increases in computational cost. In other words, MoE architectures make it possible to scale up total model size while keeping real-time operations lightweight and efficient.<\/p>\n<\/blockquote>\n\n\n\n<p id=\"1-efficiency-lower-cost-faster-speed\">1. Efficiency (Lower Cost, Faster Speed) &#8211; Only a small subset of parameters is activated for any given query. This selective computation dramatically reduces processing load (FLOPs), lowering both time and cost.  <strong>Result:<\/strong>\u00a0Faster, cheaper responses compared to dense models of similar total size.<\/p>\n\n\n\n<p id=\"2-scalability-more-parameters-less-pain\">2. Scalability (More Parameters, Less Pain) &#8211; MoE models can contain hundreds of billions of parameters overall, yet only use a fraction of them per query. <strong>Result:<\/strong>\u00a0Massive knowledge capacity without proportional increases in compute expense. &#8211; <\/p>\n\n\n\n<p id=\"3-specialization-deeper-better-knowledge\">3. Specialization (Deeper, Better Knowledge) &#8211; By segmenting expertise across distinct experts, MoE models develop specialized strengths. <strong>Result:<\/strong>\u00a0Higher accuracy, adaptability, and nuanced reasoning compared to monolithic, dense models.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"3-specialization-deeper-better-knowledge\"><strong>Mixture of Experts (MoE) models originated in 1991.<\/strong><\/h2>\n\n\n\n<p id=\"3-specialization-deeper-better-knowledge\"><br>The concept was first introduced in the seminal paper &#8220;Adaptive Mixtures of Local Expert Networks&#8221; by Robert Jacobs, Michael Jordan, Geoffrey Hinton, and others.  This early work proposed using multiple specialized &#8220;expert&#8221; networks with a gating mechanism to divide tasks efficiently\u2014laying the foundation for modern MoE architectures.<\/p>\n\n\n\n<p><strong>Key Milestones<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Early 1990s:<\/strong>\u00a0Academic roots in conditional computation, with Hinton&#8217;s team exploring ensemble-like networks.<a href=\"https:\/\/cameronrwolfe.substack.com\/p\/conditional-computation-the-birth\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/li>\n\n\n\n<li><strong>2017:<\/strong>\u00a0Noam Shazeer (with Hinton and Jeff Dean at Google) scaled MoE to a 137B-parameter LSTM model using sparse gating, marking practical NLP application.<\/li>\n\n\n\n<li><strong>2021:<\/strong>\u00a0Google&#8217;s Switch Transformer pushed to 1.6 trillion parameters, proving MoE&#8217;s scalability for transformers.<\/li>\n<\/ul>\n\n\n\n<p id=\"3-specialization-deeper-better-knowledge\">MoE remained mostly theoretical for decades due to training challenges but exploded in popularity around 2023-2024 with models like Mixtral and DeepSeek, powering today&#8217;s largest efficient LLMs<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"the-bottom-line\">The Bottom Line<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th class=\"has-text-align-left\" data-align=\"left\"><strong>Feature<\/strong><\/th><th class=\"has-text-align-left\" data-align=\"left\"><strong>Traditional Dense LLM<\/strong><\/th><th class=\"has-text-align-left\" data-align=\"left\"><strong>Mixture of Experts (MoE)<\/strong><\/th><\/tr><\/thead><tbody><tr><td><strong>Structure<\/strong><\/td><td>Everything connected to everything.<\/td><td>Specialized experts guided by a router.<\/td><\/tr><tr><td><strong>Processing<\/strong><\/td><td>All parameters used every time.<\/td><td>Only the most relevant experts activated.<\/td><\/tr><tr><td><strong>Analogy<\/strong><\/td><td>A single, brilliant generalist.<\/td><td>A specialized panel of world-class consultants.<\/td><\/tr><tr><td><strong>Benefit<\/strong><\/td><td>Powerful but costly and slow.<\/td><td>Powerful, efficient, and fast.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>In short,&nbsp;<strong>Mixture of Experts<\/strong>&nbsp;represents the next frontier in AI scalability and performance. It allows language models to reach unprecedented levels of capability\u2014combining the depth of specialization with the speed and efficiency required for practical deployment across industries.<\/p>\n\n\n\n<p>MoE isn\u2019t just an optimization. It\u2019s the breakthrough that makes&nbsp;<strong>massive, intelligent, and efficient AI<\/strong>&nbsp;a reality.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<div style=\"font-family: Verdana, Geneva, sans-serif; font-size: 11px; line-height: 1.6; color: #333;\">\n    <p>\n        <strong>Disclaimer:<\/strong> \n        <em>I personally love to share my learnings, thoughts, and ideas; I get great satisfaction knowing someone has read and benefited from an article. This content is created entirely on my own time and in a personal capacity. The views expressed here are mine alone and do not represent the positions or opinions of my employer.<\/em>\n    <\/p>\n    <p>\n        In my professional role, I serve as a Workforce Transformation Solutions Principal for \n        <a href=\"https:\/\/www.dell.com\/en-us\/work\/learn\/by-service-type-deployment\" style=\"color: #007db8; font-weight: bold; text-decoration: none;\">Dell Technology Services<\/a>. \n        I am passionate about guiding organizations through complex technology transitions and \n        <a href=\"https:\/\/www.delltechnologies.com\/en-us\/what-we-do\/workforce-transformation.htm\" style=\"color: #007db8; font-weight: bold; text-decoration: none;\">Workforce Transformation<\/a>. \n        <a href=\"https:\/\/www.delltechnologies.com\/en-us\/index.htm\" style=\"color: #007db8; font-weight: bold; text-decoration: none;\">Learn more at Dell Technologies<\/a>.\n    <\/p>\n    <hr style=\"border: 0; border-top: 1px solid #ddd; margin: 12px 0;\">\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>When you hear about the latest large language models (LLMs)\u2014like GPT-4, Claude, or Gemini\u2014it\u2019s easy to feel overwhelmed by their sheer scale. These models contain billions, sometimes trillions, of parameters. This incredible size is what gives them their broad capabilities\u2014from writing code and summarizing texts to answering complex questions and creating poetry. But that scale&#8230;<\/p>\n","protected":false},"author":2,"featured_media":520221,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_kad_blocks_custom_css":"","_kad_blocks_head_custom_js":"","_kad_blocks_body_custom_js":"","_kad_blocks_footer_custom_js":"","ngg_post_thumbnail":0,"episode_type":"","audio_file":"","podmotor_file_id":"","podmotor_episode_id":"","cover_image":"","cover_image_id":"","duration":"","filesize":"","filesize_raw":"","date_recorded":"","explicit":"","block":"","itunes_episode_number":"","itunes_title":"","itunes_season_number":"","itunes_episode_type":"","_kad_post_transparent":"","_kad_post_title":"","_kad_post_layout":"","_kad_post_sidebar_id":"","_kad_post_content_style":"","_kad_post_vertical_padding":"","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"_kad_post_classname":"","footnotes":""},"categories":[1031,441,446],"tags":[471,930,871,876,986],"class_list":["post-520219","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-learnings-series","category-tech-talk","category-tips-tools-resources","tag-ai","tag-ai-series","tag-genai","tag-llm","tag-local-ai"],"taxonomy_info":{"category":[{"value":1031,"label":"AI Learnings Series"},{"value":441,"label":"Tech Talk"},{"value":446,"label":"Tips, Tools &amp; Resources"}],"post_tag":[{"value":471,"label":"AI"},{"value":930,"label":"AI Series"},{"value":871,"label":"GenAi"},{"value":876,"label":"LLM"},{"value":986,"label":"Local AI"}]},"featured_image_src_large":["https:\/\/jorgep.com\/blog\/wp-content\/uploads\/Featured-MixtureofExperts.jpg",1024,512,false],"author_info":{"display_name":"Jorge Pereira","author_link":"https:\/\/jorgep.com\/blog\/author\/jorge\/"},"comment_info":0,"category_info":[{"term_id":1031,"name":"AI Learnings Series","slug":"ai-learnings-series","term_group":0,"term_taxonomy_id":1041,"taxonomy":"category","description":"","parent":0,"count":9,"filter":"raw","cat_ID":1031,"category_count":9,"category_description":"","cat_name":"AI Learnings Series","category_nicename":"ai-learnings-series","category_parent":0},{"term_id":441,"name":"Tech Talk","slug":"tech-talk","term_group":0,"term_taxonomy_id":451,"taxonomy":"category","description":"","parent":0,"count":678,"filter":"raw","cat_ID":441,"category_count":678,"category_description":"","cat_name":"Tech Talk","category_nicename":"tech-talk","category_parent":0},{"term_id":446,"name":"Tips, Tools &amp; Resources","slug":"tips-tools-resources","term_group":0,"term_taxonomy_id":456,"taxonomy":"category","description":"","parent":0,"count":83,"filter":"raw","cat_ID":446,"category_count":83,"category_description":"","cat_name":"Tips, Tools &amp; Resources","category_nicename":"tips-tools-resources","category_parent":0}],"tag_info":[{"term_id":471,"name":"AI","slug":"ai","term_group":0,"term_taxonomy_id":481,"taxonomy":"post_tag","description":"","parent":0,"count":147,"filter":"raw"},{"term_id":930,"name":"AI Series","slug":"ai-series","term_group":0,"term_taxonomy_id":940,"taxonomy":"post_tag","description":"","parent":0,"count":152,"filter":"raw"},{"term_id":871,"name":"GenAi","slug":"genai","term_group":0,"term_taxonomy_id":881,"taxonomy":"post_tag","description":"","parent":0,"count":83,"filter":"raw"},{"term_id":876,"name":"LLM","slug":"llm","term_group":0,"term_taxonomy_id":886,"taxonomy":"post_tag","description":"","parent":0,"count":17,"filter":"raw"},{"term_id":986,"name":"Local AI","slug":"local-ai","term_group":0,"term_taxonomy_id":996,"taxonomy":"post_tag","description":"","parent":0,"count":29,"filter":"raw"}],"_links":{"self":[{"href":"https:\/\/jorgep.com\/blog\/wp-json\/wp\/v2\/posts\/520219","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/jorgep.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/jorgep.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/jorgep.com\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/jorgep.com\/blog\/wp-json\/wp\/v2\/comments?post=520219"}],"version-history":[{"count":1,"href":"https:\/\/jorgep.com\/blog\/wp-json\/wp\/v2\/posts\/520219\/revisions"}],"predecessor-version":[{"id":520222,"href":"https:\/\/jorgep.com\/blog\/wp-json\/wp\/v2\/posts\/520219\/revisions\/520222"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/jorgep.com\/blog\/wp-json\/wp\/v2\/media\/520221"}],"wp:attachment":[{"href":"https:\/\/jorgep.com\/blog\/wp-json\/wp\/v2\/media?parent=520219"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/jorgep.com\/blog\/wp-json\/wp\/v2\/categories?post=520219"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/jorgep.com\/blog\/wp-json\/wp\/v2\/tags?post=520219"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}