{"id":189519,"date":"2025-04-17T20:05:48","date_gmt":"2025-04-17T20:05:48","guid":{"rendered":"https:\/\/peraltafinancing.com\/apple-2\/using-ollama-to-run-llms-locally\/"},"modified":"2025-04-17T20:05:48","modified_gmt":"2025-04-17T20:05:48","slug":"using-ollama-to-run-llms-locally","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=189519","title":{"rendered":"Using Ollama to Run LLMs Locally"},"content":{"rendered":"<p> <br \/>\n<\/p>\n<div>\n<p>Large Language Models (LLMs) have transformed how we interact with AI, but using them typically requires sending your data to cloud services like OpenAI\u2019s ChatGPT. For those concerned with privacy, working in environments with limited internet access, or simply wanting to avoid subscription costs, running LLMs locally is an attractive alternative.  <\/p>\n<p>With tools like Ollama, you can run large language models directly on your own hardware, maintaining full control over your data.<\/p>\n<h2 id=\"toc-anchor-001\">Getting Started<\/h2>\n<p>To follow along with this tutorial, you\u2019ll need a computer with the following specs:<\/p>\n<ul>\n<li>At least 8GB of RAM (16GB or more recommended for larger models)<\/li>\n<li>At least 10GB of free disk space<\/li>\n<li>(optional, but recommended) A dedicated GPU<\/li>\n<li>Windows, macOS, or Linux as your operating system<\/li>\n<\/ul>\n<p>The more powerful your hardware, the better your experience will be. A dedicated GPU with at least 12GB of VRAM will allow you to comfortably run most LLMs. If you have the budget, you might even want to consider a high-end GPU like a RTX 4090 or RTX 5090. Don\u2019t fret if you can\u2019t afford any of that though, Ollama will even run on a Raspberry Pi 4!<\/p>\n<h2 id=\"toc-anchor-002\">What is Ollama?<\/h2>\n<p>Ollama is an open-source, lightweight framework designed to run large language models on your local machine or server. It makes running complex AI models as simple as running a single command, without requiring deep technical knowledge of machine learning infrastructure.<\/p>\n<p>Here are some key features of Ollama:<\/p>\n<ul>\n<li>Simple command-line interface for running models<\/li>\n<li>RESTful API for integrating LLMs into your applications<\/li>\n<li>Support for models like Llama, Mistral, and Gemma<\/li>\n<li>Efficient memory management to run models on consumer hardware<\/li>\n<li>Cross-platform support for Windows, macOS, and Linux<\/li>\n<\/ul>\n<p>Unlike cloud-based solutions like ChatGPT or Claude, Ollama doesn\u2019t require an internet connection once you\u2019ve downloaded the models. A big benefit  of running LLMs locally is no usage quotas or API costs to worry about. This makes it perfect for developers wanting to experiment with LLMs, users concerned about privacy, or anyone wanting to integrate AI capabilities into offline applications.<\/p>\n<h2 id=\"toc-anchor-003\">Downloading and Installing Ollama<\/h2>\n<p>To get started with Ollama, you\u2019ll need to download and install it on your system.<\/p>\n<p>First off, visit the official Ollama website at <a href=\"https:\/\/ollama.com\/download\" target=\"_blank\" rel=\"noopener noreferrer nofollow\">https:\/\/ollama.com\/download<\/a> and select your operating system. I\u2019m using Windows, so I\u2019ll be covering that. It\u2019s very straightforward for all operating systems though, so no worries!  <\/p>\n<p>Depending on your OS, you\u2019ll either see a download button or an installation command. If you see the download button, click it to download the installer.<\/p>\n<p><a href=\"https:\/\/files.koenig.kodeco.com\/uploads\/2025\/03\/windows_download_screen.png\" target=\"_blank\" rel=\"noopener noreferrer nofollow\"><img decoding=\"async\" src=\"https:\/\/files.koenig.kodeco.com\/uploads\/2025\/03\/windows_download_screen.png\" alt=\"Windows download screen\" class=\"aligncenter size-full bordered\"\/><\/a><\/p>\n<p>Once you\u2019ve downloaded Ollama, install it on your system. On Windows, this is done via an installer. Once it opens, click the <em>Install<\/em> button and Ollama will install automatically.<\/p>\n<p><a href=\"https:\/\/files.koenig.kodeco.com\/uploads\/2025\/03\/windows_install_window.png\" target=\"_blank\" rel=\"noopener noreferrer nofollow\"><img decoding=\"async\" src=\"https:\/\/files.koenig.kodeco.com\/uploads\/2025\/03\/windows_install_window.png\" alt=\"Windows install window\" class=\"aligncenter size-full bordered\"\/><\/a><\/p>\n<p>Once installed, Ollama will start automatically and create a system tray icon.<\/p>\n<p><a href=\"https:\/\/files.koenig.kodeco.com\/uploads\/2025\/03\/tray_icon.png\" target=\"_blank\" rel=\"noopener noreferrer nofollow\"><img decoding=\"async\" src=\"https:\/\/files.koenig.kodeco.com\/uploads\/2025\/03\/tray_icon.png\" alt=\"Tray icon\" class=\"aligncenter size-full bordered\"\/><\/a><\/p>\n<p>After installation, Ollama runs as a background service and listens on <code>localhost:11434<\/code> by default. This is where the API will be accessible for other applications to connect to. You can check if the service is running correctly by opening <a href=\"http:\/\/localhost:11434\" target=\"_blank\" rel=\"noopener noreferrer nofollow\">http:\/\/localhost:11434<\/a> in your web browser. If you see a response, you\u2019re good to go!<\/p>\n<p><a href=\"https:\/\/files.koenig.kodeco.com\/uploads\/2025\/03\/ollama_running_web_browser.png\" target=\"_blank\" rel=\"noopener noreferrer nofollow\"><img decoding=\"async\" src=\"https:\/\/files.koenig.kodeco.com\/uploads\/2025\/03\/ollama_running_web_browser.png\" alt=\"Ollama is running\" class=\"aligncenter size-full bordered\"\/><\/a><\/p>\n<h2 id=\"toc-anchor-004\">Your First Chat<\/h2>\n<p>Now that Ollama is installed, it\u2019s time to download an LLM and start a conversation.<\/p>\n<p>\n<em>Note<\/em>: By default, Ollama models are stored on your C-drive on Windows and on your home directory on Linux and macOS. If you want to use a different directory, you can set the <code>OLLAMA_DATA_PATH<\/code> environment variable to point to the desired location. This is especially useful if you have limited disk space on your drive.<br \/>To do this, use the command <code>setx OLLAMA_DATA_PATH \"path\/to\/your\/directory\"<\/code> on Windows or <code>export OLLAMA_DATA_PATH=\"path\/to\/your\/directory\"<\/code> on Linux and macOS.\n<\/p>\n<p>To start a new conversation using Ollama, open a terminal or command prompt and run the following command:<\/p>\n<pre lang=\"bash\">ollama run gemma3<\/pre>\n<p>This start a new chat session with <a href=\"https:\/\/ollama.com\/library\/gemma3\" target=\"_blank\" rel=\"noopener noreferrer nofollow\">Gemma3<\/a>, a powerful and efficient 4B parameter model. When you run this command for the first time, Ollama will download the model, which may take a few minutes depending on your internet connection. You\u2019ll see a progress indicator as the model downloads  Once it\u2019s ready you\u2019ll see <code>&gt;&gt;&gt; Send a message<\/code> in the terminal:<\/p>\n<p><a href=\"https:\/\/files.koenig.kodeco.com\/uploads\/2025\/03\/ollama_chat_ready.png\" target=\"_blank\" rel=\"noopener noreferrer nofollow\"><img decoding=\"async\" src=\"https:\/\/files.koenig.kodeco.com\/uploads\/2025\/03\/ollama_chat_ready.png\" alt=\"Ollama send a message\" class=\"aligncenter size-full bordered\"\/><\/a><\/p>\n<p>Try asking a simple question:<\/p>\n<pre lang=\"bash\">&gt;&gt;&gt; What is the capital of Belgium?<\/pre>\n<p>The model will generate a response that hopefully answers your question. In my case, I got this response:<\/p>\n<pre lang=\"bash\">The capital of Belgium is **Brussels**.&#13;\n&#13;\nIt's the country's political, economic, and cultural center. \ud83d\ude0a&#13;\n&#13;\nDo you want to know anything more about Brussels?<\/pre>\n<p>You can continue the conversation by adding more questions or statements. To exit the chat, type <code>\/bye<\/code> or press <code>Ctrl+D<\/code>.<\/p>\n<p>Congratulations! You\u2019ve just had your first conversation with a locally running LLM.  <\/p>\n<h2 id=\"toc-anchor-005\">Where to Find More Models?<\/h2>\n<p>While Gemma 3 might work well for you, there are many other models available out there. Some models are better for coding for example, while others are better for conversation.<\/p>\n<h3 id=\"toc-anchor-006\">Official Ollama Models<\/h3>\n<p>The first stop for Ollama models is the <a href=\"https:\/\/ollama.com\/search\" target=\"_blank\" rel=\"noopener noreferrer nofollow\">official Ollama library<\/a>. <\/p>\n<p><a href=\"https:\/\/files.koenig.kodeco.com\/uploads\/2025\/03\/ollama_library.png\" target=\"_blank\" rel=\"noopener noreferrer nofollow\"><img decoding=\"async\" src=\"https:\/\/files.koenig.kodeco.com\/uploads\/2025\/03\/ollama_library.png\" alt=\"Ollama library\" class=\"aligncenter size-full bordered\"\/><\/a><\/p>\n<p>The library contains a wide range of models, including chat models, coding models, and more. The models get updated almost daily, so make sure to check back often.<br \/>To download and run any of these models you\u2019re interested in, check the instructions on the model page.  <\/p>\n<p>For example, you might want to try a distilled <em>deepseek-r1<\/em> model. To open the model page, click on the model name in the library.<\/p>\n<p><a href=\"https:\/\/files.koenig.kodeco.com\/uploads\/2025\/03\/open_deepseek_page.png\" target=\"_blank\" rel=\"noopener noreferrer nofollow\"><img decoding=\"async\" src=\"https:\/\/files.koenig.kodeco.com\/uploads\/2025\/03\/open_deepseek_page.png\" alt=\"Open deepseek page\" class=\"aligncenter size-full bordered\"\/><\/a><\/p>\n<p>You\u2019ll now see the different sizes available for this model (1), along with the command to run it (2) and the used parameters (3).<\/p>\n<p><a href=\"https:\/\/files.koenig.kodeco.com\/uploads\/2025\/03\/model_properties.png\" target=\"_blank\" rel=\"noopener noreferrer nofollow\"><img decoding=\"async\" src=\"https:\/\/files.koenig.kodeco.com\/uploads\/2025\/03\/model_properties.png\" alt=\"Model properties\" class=\"aligncenter size-full bordered\"\/><\/a><\/p>\n<p>Depending on your system, you can choose a smaller or a smaller variant with the dropdown on the left. If you have 16GB or more VRAM and want to experiment with a larger model, you can choose the 14B variant. Selecting <em>14b<\/em> in the dropdown will change the command next to it as well.<\/p>\n<p><a href=\"https:\/\/files.koenig.kodeco.com\/uploads\/2025\/03\/selecting_larger_model.png\" target=\"_blank\" rel=\"noopener noreferrer nofollow\"><img decoding=\"async\" src=\"https:\/\/files.koenig.kodeco.com\/uploads\/2025\/03\/selecting_larger_model.png\" alt=\"Selecting larger model\" class=\"aligncenter size-full bordered\"\/><\/a><\/p>\n<p>Choose a size you want to try and copy the command to your clipboard. Next, paste it into a terminal or command prompt to download and run the model. I went with the 8b variant for this example, so I ran the following command:<\/p>\n<pre lang=\"bash\">ollama run deepseek-r1:8b<\/pre>\n<p>Just like with Gemma 3, you\u2019ll see a progress indicator as the model downloads. Once it\u2019s ready, you\u2019ll see a <code>&gt;&gt;&gt; Send a message<\/code> prompt in the terminal.<\/p>\n<p><a href=\"https:\/\/files.koenig.kodeco.com\/uploads\/2025\/03\/running_deepseek.gif\" target=\"_blank\" rel=\"noopener noreferrer nofollow\"><img decoding=\"async\" src=\"https:\/\/files.koenig.kodeco.com\/uploads\/2025\/03\/running_deepseek.gif\" alt=\"Running deepseek\" class=\"aligncenter size-full bordered\"\/><\/a><\/p>\n<p>To test if the model works as expected, ask a question and you should get a response. I asked the same question as before:<\/p>\n<pre lang=\"bash\">&gt;&gt;&gt; What is the capital of Belgium?<\/pre>\n<p>The response I got was:<\/p>\n<pre lang=\"bash\"><think>&#13;\n&#13;\n<\/think>&#13;\n&#13;\nThe capital of Belgium is Brussels.<\/pre>\n<p>The empty <code><think\/><\/code> tags in this case are there because deepseek-r1 is a reasoning model, and it didn\u2019t need to do any reasoning to answer this particular question. Feel free to experiment with different models and questions to see what outcomes you get.<\/p>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"<p>Large Language Models (LLMs) have transformed how we interact with AI, but using them typically requires sending your data to cloud services like OpenAI\u2019s ChatGPT. For those concerned with privacy, working in environments with limited internet access, or simply wanting to avoid subscription costs, running LLMs locally is an attractive alternative. With tools like Ollama, [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":189520,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11768],"tags":[18306,31984,74074,2753],"dealstore":[],"offerexpiration":[],"class_list":["post-189519","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-apple-2","tag-llms","tag-locally","tag-ollama","tag-run"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Using Ollama to Run LLMs Locally - Som2ny Network<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fivemor.com\/?p=189519\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Using Ollama to Run LLMs Locally - Som2ny Network\" \/>\n<meta property=\"og:description\" content=\"Large Language Models (LLMs) have transformed how we interact with AI, but using them typically requires sending your data to cloud services like OpenAI\u2019s ChatGPT. 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For those concerned with privacy, working in environments with limited internet access, or simply wanting to avoid subscription costs, running LLMs locally is an attractive alternative. 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