> ## Documentation Index > Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt > Use this file to discover all available pages before exploring further. # Course Introduction > This course teaches how to run, experiment, and fine-tune large language models locally using Ollama. Generative AI is revolutionizing industries, and at the heart of this transformation are powerful large language models—whether it’s [ChatGPT](https://chat.openai.com), [Claude](https://www.anthropic.com/product/claude), or the next breakthrough. But what if you could run and manage these models on your own hardware? That’s where **Ollama** comes in. Ollama provides a seamless, end-to-end interface for running, experimenting, and fine-tuning LLMs locally. Developers, data scientists, and AI enthusiasts can integrate these models into custom applications without relying on external APIs. Welcome to **Running Local LLMs with Ollama**. I’m Arsh Sharma, and in this course you’ll progress from the fundamentals of local LLM deployment to building real-world AI applications and customizing your own models for peak performance. Let’s take a look at what’s ahead: ![The image shows a person wearing a "KodeKloud" t-shirt next to a presentation slide titled "Running Local LLMs with Ollama," which includes topics like getting started, building AI applications, and customizing models.](https://kodekloud.com/kk-media/image/upload/v1752883737/notes-assets/images/Running-Local-LLMs-With-Ollama-Course-Introduction/kodekloud-running-local-llms-ollama.jpg) | Section | Topics Covered | | ------------------------------------- | -------------------------------------------------------------------------------------------------- | | **1. Getting Started with Ollama** | Installation, CLI basics, first inference, model catalog, community tools for ChatGPT-style UI | | **2. Building AI Applications** | Ollama REST API integration, development patterns, interoperability with OpenAI API | | **3. Customizing Models with Ollama** | Modelfile configuration, parameter tuning, context window settings, registry upload and management | *** ## 1. Getting Started with Ollama In this module, you will: * Understand the key benefits of running LLMs locally * Install Ollama on macOS, Linux, or Windows Subsystem for Linux * Launch your first model inference via the CLI * Explore essential commands to list, pull, and inspect models * Leverage community-driven templates to spin up a ChatGPT-style front end Ensure you have at least 8 GB of RAM, 20 GB of free disk space, and a modern x86\_64 or ARM64 processor. Familiarity with the command line is recommended. *** ## 2. Building AI Applications Once Ollama is up and running, you’ll learn how to integrate it into your applications: * Explore key API endpoints for model metadata and text generation * Integrate with popular languages and frameworks (Node.js, Python, Go) * Compare Ollama’s request/response patterns with the OpenAI API * Complete hands-on exercises to build a simple chat interface *** ## 3. Customizing Models with Ollama Tailor your language models to specific tasks by adjusting parameters and training data: * Define custom settings in a Modelfile * Tune performance options like temperature and context length * Use your own dataset to fine-tune or prompt-tune models * Manage model versions in the Ollama registry for team collaboration *** ## Links and References * [Ollama CLI Documentation](https://ollama.com/docs/cli) * [Ollama REST API Guide](https://ollama.com/docs/api) * [OpenAI API Reference](https://platform.openai.com/docs/api-reference) * [KodeKloud Community Forum](/community) Let’s dive in and unlock the full potential of AI with Ollama!