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LLM Crash Course: Run Models Locally. Master LLM Engineering
Rating: 4.4 out of 5(22 ratings)
106 students

LLM Crash Course: Run Models Locally. Master LLM Engineering

Explore LLM Engineering hands-on. Run and build GenAI apps locally, offline, and without any cloud or subscription.
Created byGanesh S
Last updated 6/2025
English

What you'll learn

  • Set up and run open-source LLMs locally on Windows, macOS, or Linux with no cloud, no API keys, and zero recurring costs.
  • Install and configure tools like Python, Poetry, and model runtimes to deploy and manage LLMs in a fully offline environment
  • Use Python to send prompts and receive responses from local LLMs, simulating real conversations through structured message flows.
  • Understand LLM roles, token limits, context windows, streaming responses, and prompt design for better model control.
  • Build LLM tools like a customer support agent using function calling and structured responses.
  • Create a simple Retrieval-Augmented Generation (RAG) app to enhance your LLM with external data for better context-aware outputs.

Course content

2 sections • 16 lectures • 3h 48m total length

  • Introduction7:49

    Get a quick overview of the course, its structure, learning outcomes, and what you can expect by the end—including running LLMs locally without cloud dependency.

  • Hardware Requirement for Running LLM Model Offline3:08

    Learn the recommended CPU, RAM, GPU, and disk requirements to run modern language models efficiently on your machine — without relying on the cloud. We'll also cover real-world performance tips and tradeoffs.

  • Preview of the Offline Model Running on My Mac5:38

    See it in action! This lecture gives you a sneak peek of the course’s final result — a fully functional, offline LLM running on a local Mac. Visualize what you'll build and get inspired to follow the setup journey.

  • About the Tool for Running Model Locally6:13

    Explore the lightweight, open-source tooling (like Ollama or alternatives) that powers offline LLM execution. Learn about model variants, architecture support, and how it works under the hood.

  • Setup and Connecting to the Model On Windows11:45

    Follow step-by-step instructions to install, configure, and verify your local LLM setup on a Windows machine.

  • Setup and Connecting to the Model On Ubuntu12:58

    Learn how to set up and run the local LLM model on Ubuntu/Linux-based systems, including dealing with common environment and dependency issues.

  • Setup and Connecting to the Model On Mac10:32

    Mac users—this one’s for you. Get a hands-on walkthrough for setting up your model locally and troubleshooting any OS-specific issues.

Requirements

  • Knowledge of Python programming — you should know how to install python, write, debug and run Python scripts.
  • Familiarity with the command line or terminal — you’ll use basic commands during setup and testing.
  • A computer running Windows, macOS, or Linux — the course includes platform-specific setup instructions.
  • At least 8GB of RAM (16GB recommended) — running language models locally requires a reasonable amount of memory.
  • A stable internet connection for the initial setup only — after that, everything runs 100% offline.
  • No prior experience with LLMs, AI, or machine learning is required — all key concepts are explained with hands-on examples.

Description

Tired of relying on cloud-based AI tools that require subscriptions, API keys, and constant internet access to run LLMs? Why hold your LLM Enggineering journey?

In this hands-on, fast-track course, you'll learn how to run powerful open-source language models locally on your own machine — 100% offline, private, and free forever. No recurring costs. No third-party services. Just you, your laptop, and your own AI environment.

I’ll guide you through the exact setup process step-by-step on Windows, macOS, and Linux. Then, I will go beyond just setup — you'll also explore real-world LLM features like tools, Retrieval-Augmented Generation (RAG), streaming responses, and how to interact with LLMs using Python scripts and prompts.

Whether you're a student, developer, or tech pro, this course empowers you to take full control of your LLM learning and workflows — without vendor lock-in or cloud dependency.

What You’ll Learn:

  • Set up a local environment to run cutting-edge LLMs.

  • Use simple command-line tools to download and manage models.

  • Write Python scripts to interact with and prompt local models.

  • Learn Prompt Engineering with handson coding examples and understand how it impacts llm applications

  • Explore key LLM concepts like RAG using LangChain, tools(callable functions), streaming, embeddings, vector databases and prompt engineering.

  • Build a privacy-first, reusable LLM setup for internal tools, research, or personal projects.

  • Avoid API keys, subscriptions, and internet dependency — forever.

Who This Course Is For:

  • Engineers & developers familiar with Python basics.

  • Students or professionals looking to learn LLMs in a private, offline environment.

  • Organizations exploring internal AI tools without relying on external APIs.

I skip the fluff and unnecessary theory — this is a practical, no-nonsense crash course for modern LLM engineering with a unique offline-first approach.

By the end, you’ll not only be running models entirely on your computer, but also have learned practical LLM concepts that you can apply across real-world environments. Let’s get started!

Who this course is for:

  • Developers and engineers who want to run powerful open-source LLMs locally without relying on cloud services, APIs, or subscriptions.
  • Python programmers looking to integrate language models into scripts, tools, or real-world applications in a fully offline environment.
  • Students or tech enthusiasts eager to explore AI and LLMs through a hands-on, practical course — without deep ML or data science background.
  • Makers, hackers, and open-source contributors who want full control over their AI tools and workflows with privacy and portability in mind.
  • Professionals in organizations exploring internal LLM applications for automation, chatbots, and document handling with no data leaving their system.
  • Anyone looking to quickly get up and running with LLMs, tools, prompt engineering, RAG, and chat-like experiences — all in a focused, crash-course format.

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