{"id":788,"date":"2026-06-06T01:59:11","date_gmt":"2026-06-06T01:59:11","guid":{"rendered":"https:\/\/convly.ai\/best-local-llms-to-run-on-ollama-2026\/"},"modified":"2026-06-06T01:59:11","modified_gmt":"2026-06-06T01:59:11","slug":"best-local-llms-to-run-on-ollama-2026","status":"publish","type":"post","link":"https:\/\/convly.ai\/fr\/best-local-llms-to-run-on-ollama-2026\/","title":{"rendered":"The Best Local LLMs to Run on Ollama in 2026 (Ranked by Use Case)"},"content":{"rendered":"<p>Ollama can run more than a hundred models, which is exactly why people freeze when picking one. The good news: you only need a handful. This guide ranks the best local LLMs in 2026 by the job you&#8217;re trying to do \u2014 general work, coding, reasoning, or squeezing onto weak hardware \u2014 and tells you the memory each one needs.<\/p>\n<p>New here? Start with <a href=\"https:\/\/convly.ai\/fr\/what-is-ollama-complete-guide-2026\/\">what Ollama is<\/a>, then <a href=\"https:\/\/convly.ai\/fr\/ollama-system-requirements-2026\/\">check your hardware<\/a> before downloading anything.<\/p>\n<div class=\"convly-tldr\">\n<h3>Principaux enseignements<\/h3>\n<ul>\n<li><strong>Best all-rounder:<\/strong> <strong>Gemma 4 26B A4B<\/strong> \u2014 tool calling + vision, runs comfortably, the most practical pick for most people. <code>ollama run gemma4<\/code><\/li>\n<li><strong>Best for coding:<\/strong> <strong>Qwen 3.6 27B<\/strong> \u2014 the strongest dense coding model at ~77% SWE-bench, needs ~22 GB VRAM.<\/li>\n<li><strong>Best for reasoning\/math:<\/strong> <strong>DeepSeek-R1 7B<\/strong> \u2014 best chain-of-thought performance you can run small.<\/li>\n<li><strong>Best for weak hardware:<\/strong> <strong>Gemma2 2B<\/strong> \u2014 runs on ~1.7 GB RAM, fine on a CPU-only laptop.<\/li>\n<li><strong>Safest commercial license:<\/strong> Qwen 3 and Gemma 4 ship under Apache 2.0.<\/li>\n<\/ul>\n<\/div>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_84 counter-flat ez-toc-counter ez-toc-container-direction\">\n<label for=\"ez-toc-cssicon-toggle-item-6a23c7e911aff\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #000000;color:#000000\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #000000;color:#000000\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewbox=\"0 0 24 24\" version=\"1.2\" baseprofile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-6a23c7e911aff\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/convly.ai\/fr\/best-local-llms-to-run-on-ollama-2026\/#How_to_think_about_picking_a_model\" >How to think about picking a model<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/convly.ai\/fr\/best-local-llms-to-run-on-ollama-2026\/#Best_all-rounder_Gemma_4_26B_A4B\" >Best all-rounder: Gemma 4 26B A4B<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/convly.ai\/fr\/best-local-llms-to-run-on-ollama-2026\/#Best_for_coding_Qwen_36_27B\" >Best for coding: Qwen 3.6 27B<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/convly.ai\/fr\/best-local-llms-to-run-on-ollama-2026\/#Best_for_reasoning_and_math_DeepSeek-R1_7B\" >Best for reasoning and math: DeepSeek-R1 7B<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/convly.ai\/fr\/best-local-llms-to-run-on-ollama-2026\/#Best_for_weak_hardware_Gemma2_2B\" >Best for weak hardware: Gemma2 2B<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/convly.ai\/fr\/best-local-llms-to-run-on-ollama-2026\/#Best_for_enterprise_scale_Qwen3_235B-A22B\" >Best for enterprise scale: Qwen3 235B-A22B<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/convly.ai\/fr\/best-local-llms-to-run-on-ollama-2026\/#Quick_comparison\" >Quick comparison<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/convly.ai\/fr\/best-local-llms-to-run-on-ollama-2026\/#A_simple_decision_path\" >A simple decision path<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/convly.ai\/fr\/best-local-llms-to-run-on-ollama-2026\/#FAQ\" >FAQ<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/convly.ai\/fr\/best-local-llms-to-run-on-ollama-2026\/#Bottom_line\" >R\u00e9sultat<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"How_to_think_about_picking_a_model\"><\/span>How to think about picking a model<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Three things decide which model is &#8220;best&#8221; for you, in this order:<\/p>\n<ol>\n<li><strong>What can your hardware fit?<\/strong> A model has to fit in your RAM or VRAM (in quantized form). The best model you <em>can&#8217;t<\/em> run is useless. Match the size to your machine using our <a href=\"https:\/\/convly.ai\/fr\/ollama-system-requirements-2026\/\">system requirements guide<\/a>.<\/li>\n<li><strong>What&#8217;s the job?<\/strong> Coding, general chat, reasoning, and document work reward different models. A great coder isn&#8217;t always a great writer.<\/li>\n<li><strong>Does the license matter?<\/strong> If you&#8217;re building a product, prefer Apache 2.0 models (Qwen 3, Gemma 4) over more restrictively licensed ones.<\/li>\n<\/ol>\n<h2><span class=\"ez-toc-section\" id=\"Best_all-rounder_Gemma_4_26B_A4B\"><\/span>Best all-rounder: Gemma 4 26B A4B<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Google&#8217;s <strong>Gemma 4 26B A4B<\/strong> (released April 2026) is the model we&#8217;d put in most people&#8217;s hands first. It&#8217;s a mixture-of-experts design with built-in tool calling and vision support, and it punches well above its memory footprint \u2014 making it ideal for local agents, function calling, and structured output. It&#8217;s Apache 2.0, so you can build on it commercially.<\/p>\n<pre><code>ollama run gemma4\n<\/code><\/pre>\n<p>If you want a single model for chat, light coding, summarizing, and agent work, this is the safe default.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Best_for_coding_Qwen_36_27B\"><\/span>Best for coding: Qwen 3.6 27B<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>For writing and refactoring code locally \u2014 without sending a line to an API \u2014 <strong>Qwen 3.6 27B<\/strong> is the strongest dense coding model you can run, landing around <strong>77% on SWE-bench<\/strong> and needing roughly <strong>22 GB of VRAM<\/strong>. If your machine can hold it, it&#8217;s the closest thing to a cloud coding assistant that never phones home.<\/p>\n<p>Running on tighter hardware? Drop to a smaller Qwen coder variant or use Gemma 4. For the full breakdown of coding-specific picks and how they compare on real tasks, see our guide to the <a href=\"https:\/\/convly.ai\/fr\/best-local-llm-for-coding-2026\/\">best local LLM for coding<\/a>.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Best_for_reasoning_and_math_DeepSeek-R1_7B\"><\/span>Best for reasoning and math: DeepSeek-R1 7B<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong>DeepSeek-R1 7B<\/strong> is a chain-of-thought model that delivers the best local math and reasoning performance at the 7B size. Because it &#8220;thinks&#8221; through problems step by step, it&#8217;s the one to reach for when correctness on multi-step logic matters more than speed. At 7B it fits on modest hardware, which makes it an unusually accessible reasoning model.<\/p>\n<pre><code>ollama run deepseek-r1\n<\/code><\/pre>\n<h2><span class=\"ez-toc-section\" id=\"Best_for_weak_hardware_Gemma2_2B\"><\/span>Best for weak hardware: Gemma2 2B<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>No discrete GPU? <strong>Gemma2 2B<\/strong> is the fastest CPU-inference option and needs only about <strong>1.7 GB of RAM<\/strong>. It won&#8217;t win benchmarks, but it&#8217;s genuinely usable for summarization, simple Q&amp;A, and drafting on a basic laptop \u2014 proof that you don&#8217;t need a workstation to start with local AI.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Best_for_enterprise_scale_Qwen3_235B-A22B\"><\/span>Best for enterprise scale: Qwen3 235B-A22B<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>If you have serious hardware and want a frontier-class open model with a clean license, <strong>Qwen3 235B-A22B<\/strong> is one of the safest enterprise picks: a mixture-of-experts model with 235B total parameters but only 22B active per token, under Apache 2.0. It&#8217;s well suited to multilingual apps and commercial products \u2014 provided you have the memory to host it.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Quick_comparison\"><\/span>Quick comparison<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>Mod\u00e8le<\/th>\n<th>Meilleur pour<\/th>\n<th>Rough memory<\/th>\n<th>Licence<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Gemma 4 26B A4B<\/td>\n<td>General \/ agents \/ vision<\/td>\n<td>Mid-range GPU<\/td>\n<td>Apache 2.0<\/td>\n<\/tr>\n<tr>\n<td>Qwen 3.6 27B<\/td>\n<td>Codage<\/td>\n<td>~22 GB VRAM<\/td>\n<td>Apache 2.0<\/td>\n<\/tr>\n<tr>\n<td>DeepSeek-R1 7B<\/td>\n<td>Reasoning \/ math<\/td>\n<td>Modest<\/td>\n<td>MIT<\/td>\n<\/tr>\n<tr>\n<td>Gemma2 2B<\/td>\n<td>Weak \/ CPU-only hardware<\/td>\n<td>~1.7 GB RAM<\/td>\n<td>Gemma license<\/td>\n<\/tr>\n<tr>\n<td>Qwen3 235B-A22B<\/td>\n<td>Enterprise \/ multilingual<\/td>\n<td>Very high<\/td>\n<td>Apache 2.0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2><span class=\"ez-toc-section\" id=\"A_simple_decision_path\"><\/span>A simple decision path<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><strong>One model for everything \u2192<\/strong> Gemma 4.<\/li>\n<li><strong>Mostly coding, strong GPU \u2192<\/strong> Qwen 3.6 27B.<\/li>\n<li><strong>Hard reasoning or math \u2192<\/strong> DeepSeek-R1.<\/li>\n<li><strong>Old laptop, no GPU \u2192<\/strong> Gemma2 2B.<\/li>\n<li><strong>Building a commercial product \u2192<\/strong> stick to the Apache 2.0 models (Qwen 3, Gemma 4).<\/li>\n<\/ul>\n<p>Whichever you choose, the command is the same \u2014 <code>ollama run &lt;model&gt;<\/code> \u2014 and you can keep several installed and switch freely. To run any of them, you&#8217;ll first need Ollama set up: here&#8217;s our <a href=\"https:\/\/convly.ai\/fr\/how-to-install-ollama-2026\/\">step-by-step install guide<\/a>.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"FAQ\"><\/span>FAQ<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3>What is the best Ollama model in 2026?<\/h3>\n<p>For most people, Gemma 4 26B A4B \u2014 it&#8217;s a capable all-rounder with tool calling and vision, an Apache 2.0 license, and a reasonable memory footprint. For coding specifically, Qwen 3.6 27B is stronger; for reasoning, DeepSeek-R1.<\/p>\n<h3>What&#8217;s the best local LLM for low-end hardware?<\/h3>\n<p>Gemma2 2B. It runs in about 1.7 GB of RAM and works on CPU-only laptops. If you have a little more headroom, a 7\u20138B model like DeepSeek-R1 7B gives noticeably better quality while still fitting modest machines.<\/p>\n<h3>Which local model is closest to ChatGPT?<\/h3>\n<p>The largest open models you can host \u2014 like Qwen3 235B-A22B \u2014 close much of the gap, but on the hardest reasoning tasks the best cloud frontier models still lead. For everyday chat, coding, and document work, a well-chosen local model is more than good enough and keeps your data private.<\/p>\n<h3>Do I need a powerful GPU for these models?<\/h3>\n<p>It depends on the model. Gemma2 2B runs on a CPU; a 7B model is comfortable on 8 GB of memory; Qwen 3.6 27B wants ~22 GB of VRAM. Match the model to your hardware using our <a href=\"https:\/\/convly.ai\/fr\/ollama-system-requirements-2026\/\">system requirements guide<\/a>.<\/p>\n<h3>Are these models free for commercial use?<\/h3>\n<p>Qwen 3 and Gemma 4 ship under Apache 2.0, which is permissive for commercial use. DeepSeek-R1 is MIT-licensed. Always confirm the specific model&#8217;s license before shipping a product, since terms can vary by release.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Bottom_line\"><\/span>R\u00e9sultat<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>You don&#8217;t need to test a hundred models \u2014 you need the right four or five. Run Gemma 4 as your default, Qwen 3.6 when you&#8217;re coding, DeepSeek-R1 when you need to reason, and Gemma2 2B when hardware is tight. Each is a single <code>ollama run<\/code> away, and all of them keep your data on your own machine.<\/p>","protected":false},"excerpt":{"rendered":"<p>Ollama can run 100+ models, but you only need a handful. Here are the best local LLMs in 2026 ranked by what you&#8217;re actually trying to do \u2014 and the VRAM each one needs.<\/p>","protected":false},"author":1,"featured_media":794,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[3],"tags":[629,630,633,632,631,606],"class_list":["post-788","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-llms","tag-best-local-llm","tag-best-ollama-models","tag-deepseek-r1","tag-gemma-4","tag-ollama-models","tag-qwen-3"],"_links":{"self":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/788","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/comments?post=788"}],"version-history":[{"count":0,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/788\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/media\/794"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/media?parent=788"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/categories?post=788"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/tags?post=788"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}