{"id":789,"date":"2026-06-06T01:59:12","date_gmt":"2026-06-06T01:59:12","guid":{"rendered":"https:\/\/convly.ai\/how-to-install-ollama-2026\/"},"modified":"2026-06-06T01:59:12","modified_gmt":"2026-06-06T01:59:12","slug":"how-to-install-ollama-2026","status":"publish","type":"post","link":"https:\/\/convly.ai\/ar\/how-to-install-ollama-2026\/","title":{"rendered":"How to Install Ollama in 2026: Mac, Windows, and Linux (Step by Step)"},"content":{"rendered":"<p>Installing Ollama is genuinely a two-minute job on every major operating system. This guide gives you the exact steps for Mac, Windows, and Linux, shows you how to run your first model, and covers the handful of errors people actually run into.<\/p>\n<p>New to the tool entirely? Start with <a href=\"https:\/\/convly.ai\/ar\/what-is-ollama-complete-guide-2026\/\">what Ollama is and how it works<\/a>, then come back here to install it.<\/p>\n<div class=\"convly-tldr\">\n<h3>\u0627\u0644\u0648\u062c\u0628\u0627\u062a \u0627\u0644\u0631\u0626\u064a\u0633\u064a\u0629<\/h3>\n<ul>\n<li><strong>Mac:<\/strong> download the app from ollama.com, or <code>brew install ollama<\/code>.<\/li>\n<li><strong>Windows:<\/strong> download and run the official installer \u2014 native, no WSL required.<\/li>\n<li><strong>Linux:<\/strong> one command \u2014 <code>curl -fsSL https:\/\/ollama.com\/install.sh | sh<\/code>.<\/li>\n<li><strong>First model:<\/strong> <code>ollama run gemma4<\/code> downloads and runs a strong all-rounder.<\/li>\n<li><strong>Check it works:<\/strong> the API answers at <code>http:\/\/localhost:11434<\/code>.<\/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-6a23c77f615ff\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">\u062a\u0628\u062f\u064a\u0644<\/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-6a23c77f615ff\"  aria-label=\"\u062a\u0628\u062f\u064a\u0644\" \/><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\/ar\/how-to-install-ollama-2026\/#Before_you_install_can_your_machine_run_it\" >Before you install: can your machine run it?<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/convly.ai\/ar\/how-to-install-ollama-2026\/#Install_on_macOS\" >Install on macOS<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/convly.ai\/ar\/how-to-install-ollama-2026\/#Install_on_Windows\" >Install on Windows<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/convly.ai\/ar\/how-to-install-ollama-2026\/#Install_on_Linux\" >Install on Linux<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/convly.ai\/ar\/how-to-install-ollama-2026\/#Run_your_first_model\" >Run your first model<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/convly.ai\/ar\/how-to-install-ollama-2026\/#Verify_the_API_is_running\" >Verify the API is running<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/convly.ai\/ar\/how-to-install-ollama-2026\/#Common_install_problems_and_fixes\" >Common install problems and fixes<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/convly.ai\/ar\/how-to-install-ollama-2026\/#FAQ\" >\u0627\u0644\u0623\u0633\u0626\u0644\u0629 \u0627\u0644\u0634\u0627\u0626\u0639\u0629<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/convly.ai\/ar\/how-to-install-ollama-2026\/#Bottom_line\" >\u062e\u0644\u0627\u0635\u0629 \u0627\u0644\u0642\u0648\u0644<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Before_you_install_can_your_machine_run_it\"><\/span>Before you install: can your machine run it?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Ollama itself is tiny, but the <em>\u0627\u0644\u0645\u0648\u062f\u064a\u0644\u0627\u062a<\/em> are not. A quick rule of thumb: you want roughly as much free RAM (or VRAM) as the quantized model size \u2014 about 4\u20135 GB for a 7B model, 8 GB for a 13B model, and far more for the big ones. If you&#8217;re not sure what your hardware can handle, read our <a href=\"https:\/\/convly.ai\/ar\/ollama-system-requirements-2026\/\">Ollama system requirements guide<\/a> first so you pick a model that actually fits.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Install_on_macOS\"><\/span>Install on macOS<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The easiest path is the native app:<\/p>\n<ol>\n<li>Go to <strong>ollama.com\/download<\/strong> and download the macOS app.<\/li>\n<li>Open the <code>.dmg<\/code> and drag Ollama to Applications.<\/li>\n<li>Launch it \u2014 Ollama runs in the background and the <code>ollama<\/code> command becomes available in your terminal.<\/li>\n<\/ol>\n<p>Prefer the command line? Use Homebrew:<\/p>\n<pre><code>brew install ollama\n<\/code><\/pre>\n<p>On Apple Silicon (M1\u2013M5), Ollama automatically uses the GPU through Apple&#8217;s MLX backend (since v0.19), so you get fast inference with no extra configuration.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Install_on_Windows\"><\/span>Install on Windows<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Ollama runs natively on Windows \u2014 you no longer need WSL:<\/p>\n<ol>\n<li>Download the Windows installer from <strong>ollama.com\/download<\/strong>.<\/li>\n<li>Run the <code>.exe<\/code> and follow the prompts.<\/li>\n<li>Open <strong>PowerShell<\/strong> \u0623\u0648 <strong>Command Prompt<\/strong> and type <code>ollama --version<\/code> to confirm it&#8217;s installed.<\/li>\n<\/ol>\n<p>If you have an NVIDIA GPU, Ollama detects it automatically and uses CUDA. No driver gymnastics required, as long as your GPU drivers are current.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Install_on_Linux\"><\/span>Install on Linux<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>One command does everything:<\/p>\n<pre><code>curl -fsSL https:\/\/ollama.com\/install.sh | sh\n<\/code><\/pre>\n<p>This installs Ollama and sets it up as a <code>systemd<\/code> service that starts on boot. To confirm it&#8217;s running:<\/p>\n<pre><code>systemctl status ollama\n<\/code><\/pre>\n<p>On Ubuntu and most distros, the installer detects NVIDIA and AMD GPUs and configures the right backend. For AMD cards specifically, make sure ROCm is installed \u2014 see our deep dive on <a href=\"https:\/\/convly.ai\/ar\/amd-rocm-vs-nvidia-cuda-2026\/\">ROCm vs CUDA<\/a> for the state of AMD support in 2026.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Run_your_first_model\"><\/span>Run your first model<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>With Ollama installed, pull and run a model in one command:<\/p>\n<pre><code>ollama run gemma4\n<\/code><\/pre>\n<p>The first run downloads the model (a few gigabytes), then drops you into a chat prompt. Type a question, get an answer \u2014 entirely on your machine. Some useful commands:<\/p>\n<ul>\n<li><code>ollama list<\/code> \u2014 show models you&#8217;ve downloaded.<\/li>\n<li><code>ollama pull qwen3<\/code> \u2014 download a model without running it.<\/li>\n<li><code>ollama rm gemma4<\/code> \u2014 delete a model to reclaim disk space.<\/li>\n<li><code>ollama ps<\/code> \u2014 see what&#8217;s currently loaded in memory.<\/li>\n<\/ul>\n<p>Not sure which model to start with? Our guide to the <a href=\"https:\/\/convly.ai\/ar\/best-local-llms-to-run-on-ollama-2026\/\">best local LLMs on Ollama<\/a> matches models to use cases and hardware.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Verify_the_API_is_running\"><\/span>Verify the API is running<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Ollama exposes a REST API on port 11434. To confirm it&#8217;s live, run:<\/p>\n<pre><code>curl http:\/\/localhost:11434\/api\/tags\n<\/code><\/pre>\n<p>A JSON response listing your models means everything works. This endpoint is what your own apps will talk to \u2014 and because Ollama offers an OpenAI-compatible API, a lot of existing code works by just changing the base URL.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Common_install_problems_and_fixes\"><\/span>Common install problems and fixes<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><strong>&#8220;ollama: command not found&#8221; (Mac\/Linux):<\/strong> the app installed but isn&#8217;t on your <code>PATH<\/code>. On Mac, make sure the app has been launched once; on Linux, open a new shell after install.<\/li>\n<li><strong>Model downloads are slow or stall:<\/strong> Ollama pulls large files; a stalled pull usually resolves with <code>ollama pull &lt;model&gt;<\/code> again \u2014 it resumes rather than restarting.<\/li>\n<li><strong>GPU not being used:<\/strong> check <code>ollama ps<\/code> \u2014 if it shows 100% CPU, your GPU drivers may be out of date or the model is too large to fit in VRAM and spilled to CPU. Try a smaller or more heavily quantized model.<\/li>\n<li><strong>&#8220;out of memory&#8221; errors:<\/strong> the model is bigger than your available RAM\/VRAM. Pull a smaller quant (look for <code>q4<\/code> variants) or a smaller model size. Our <a href=\"https:\/\/convly.ai\/ar\/ollama-system-requirements-2026\/\">system requirements guide<\/a> shows what fits where.<\/li>\n<li><strong>Port 11434 already in use:<\/strong> another Ollama instance is running. Stop it (<code>ollama ps<\/code> then quit the app\/service) before starting a new one.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"FAQ\"><\/span>\u0627\u0644\u0623\u0633\u0626\u0644\u0629 \u0627\u0644\u0634\u0627\u0626\u0639\u0629<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3>How do I install Ollama on Windows?<\/h3>\n<p>Download the native installer from ollama.com\/download and run the <code>.exe<\/code>. Ollama runs natively on Windows with no WSL required, and automatically uses an NVIDIA GPU via CUDA if you have one. Confirm the install with <code>ollama --version<\/code> in PowerShell.<\/p>\n<h3>How do I install Ollama on Linux?<\/h3>\n<p>Run <code>curl -fsSL https:\/\/ollama.com\/install.sh | sh<\/code>. This installs Ollama and registers it as a systemd service. Verify it with <code>systemctl status ollama<\/code>. The installer auto-detects NVIDIA and AMD GPUs.<\/p>\n<h3>Can I install Ollama with Homebrew?<\/h3>\n<p>\u0646\u0639\u0645 - \u0646\u0639\u0645 - <code>brew install ollama<\/code> works on macOS. The native app from ollama.com is equally good and includes a menu-bar presence; the Homebrew route is handy if you manage everything through the command line.<\/p>\n<h3>Where does Ollama store models?<\/h3>\n<p>By default, on Mac and Linux in <code>~\/.ollama\/models<\/code>, and on Windows under your user profile. Models can be several gigabytes each, so use <code>ollama list<\/code> to track what you&#8217;ve downloaded and <code>ollama rm &lt;model&gt;<\/code> to clean up.<\/p>\n<h3>Is Ollama safe to install?<\/h3>\n<p>Yes. Ollama is open-source (MIT-licensed) and widely used. The standard caution applies to the Linux one-line installer \u2014 it&#8217;s the project&#8217;s official script, but if you prefer, you can download and inspect <code>install.sh<\/code> before running it.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Bottom_line\"><\/span>\u062e\u0644\u0627\u0635\u0629 \u0627\u0644\u0642\u0648\u0644<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>On any operating system, installing Ollama is a download-and-run affair that takes about two minutes, and your first local model is one command away. Pick a model that fits your hardware, confirm the API answers on port 11434, and you&#8217;ve got a private, free LLM running on your own machine. From here, explore <a href=\"https:\/\/convly.ai\/ar\/best-local-llms-to-run-on-ollama-2026\/\">which models to run<\/a> \u0648 <a href=\"https:\/\/convly.ai\/ar\/ollama-system-requirements-2026\/\">how much hardware each one needs<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"<p>Installing Ollama takes about two minutes on any OS. Here are the exact steps for Mac, Windows, and Linux, plus how to run your first model and fix the errors people actually hit.<\/p>","protected":false},"author":1,"featured_media":795,"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":[637,634,256,635,638,636],"class_list":["post-789","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-llms","tag-how-to-install-ollama","tag-install-ollama","tag-local-llm","tag-ollama-install","tag-ollama-setup","tag-ollama-tutorial"],"_links":{"self":[{"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/posts\/789","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/comments?post=789"}],"version-history":[{"count":0,"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/posts\/789\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/media\/795"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/media?parent=789"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/categories?post=789"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/tags?post=789"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}