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Run GPT-OSS Locally with Ollama: A Practical Guide
Rating: 4.0 out of 5(1 rating)
4 students

Run GPT-OSS Locally with Ollama: A Practical Guide

Mastering Local AI: Build AI Apps with Ollama & Llama Models
Created byIshaq Jan
Last updated 8/2025
English

What you'll learn

  • Set up and install Ollama on Windows
  • Run popular open-source LLMs like GPT-OSS and Llama Models
  • Use Ollama with APIs and build simple AI-powered apps locally
  • Understand the benefits and limitations of running AI models offline

Course content

1 section • 5 lectures • 59m total length
  • Introduction13:51
  • Installation of Ollama3:59
  • Using Llama Models on Local Machine4:37
  • Build a Flask API Endpoint & Gradio Frontend for Ollama Model11:02
  • Build a RAG based Chatbot with Ollama25:35

Requirements

  • Basic computer usage skills and curiosity about AI
  • No prior experience with machine learning or LLMs is required
  • A PC or laptop with at least 8GB RAM (16GB recommended for best performance)
  • Internet connection for downloading models during setup

Description

Artificial Intelligence is no longer just a cloud-based service — with the right tools, you can run powerful AI models directly on your own computer. In this course, you’ll learn how to set up, run, and customize Llama 3.2 and other open-source GPT-style models locally using Ollama.

We’ll start with the basics — installing Ollama, downloading and running different AI models, and understanding how local AI works compared to cloud-based solutions. Then, we’ll move into practical skills like prompt engineering, model configuration, and hardware optimization so you can get the best performance from your setup.

You’ll also learn how to integrate these models into your own applications, from simple scripts to full AI-powered tools. The course covers real-world examples, troubleshooting tips, and customization options that allows you to tailor the AI’s behavior to your needs.

By the end of this course, you will:

  • Confidently run Llama 3.2 and other models locally

  • Customize models for speed, accuracy, and task-specific results

  • Build AI-powered applications without relying on cloud APIs

  • Build RAG based chatbot

  • Learn different types of vector database like Pinecone

  • Learn the concepts of RAG and embedding models

Whether you’re a developer, AI enthusiast, or researcher, this course will give you the hands-on experience you need to master local AI and start building intelligent applications — all from your own machine.

Who this course is for:

  • Developers, hobbyists, or students interested in running AI models locally
  • Anyone who wants to avoid cloud API costs and data privacy concerns
  • Beginners looking to explore open-source alternatives to ChatGPT