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Local AI Revolution: Ollama and OpenClaw

2h 51m 58s
English
Paid
Added to CourseFlix

Local AI Revolution: Ollama and OpenClaw is a 24-lesson 2 hours 51 minutes self-paced course by Zero To Mastery. Want to build your own private AI platform without the limitations of cloud services and external APIs?

Course facts

Lessons
24
Duration
2 hours 51 minutes
Language
English
Added to CourseFlix
October 2026
Instructor
Zero To Mastery
Price
Premium

Want to build your own private AI platform without the limitations of cloud services and external APIs? In this practical course, you will learn how to deploy local LLMs, accelerate their performance on GPU, and create autonomous AI agents based on Ollama and OpenClaw.

What you will learn in this course

The course is structured on the principle of "from zero to a fully operational system" and is suitable even for those who are setting up local models for the first time.

1. Deploying Ollama and local LLMs

You will step-by-step install Ollama on a Linux server and learn how to work with modern open language models in a private environment.

  • Installation and basic setup of Ollama
  • Loading and running popular LLMs
  • Building a private AI environment without external dependencies

2. Creating a convenient interface through Open WebUI

To manage the models comfortably, you will set up the Open WebUI web panel.

  • Deploying a frontend to work with models
  • Using chats, presets, and additional tools
  • Setting up access for different users

3. Working with GPU and renting servers

You will learn how to transfer AI load from CPU to GPU and achieve significant performance gains.

  • Setting up GPU acceleration
  • Selecting and renting GPU servers
  • Optimizing resources for large models

Automation with OpenClaw

The second part of the course is dedicated to creating autonomous systems and AI agents.

4. Deploying an AI agent on OpenClaw

You will create an intelligent assistant that can perform tasks, interact with files, and integrate with other services.

  • Installation and setup of OpenClaw
  • Creation and configuration of an AI agent
  • Connecting tools, external actions, and scripts

5. Integrating the AI agent with Telegram

You will configure a convenient chatbot that will handle user requests in real time.

  • Creating a Telegram bot
  • Connecting it to the OpenClaw agent
  • Setting up dialogues and automating scenarios

What you will get after completing the course

By the end of the training, you will have a fully prepared local AI platform, operating on your infrastructure.

  • Full control over data and computations
  • The ability to deploy any open models
  • Flexibility to create your own intelligent services
  • An expandable system for experiments and development

Master local artificial intelligence and create your own autonomous ecosystem without the limitations of third-party platforms.

Who teaches Local AI Revolution: Ollama and OpenClaw? Zero To Mastery

Zero To Mastery thumbnail

Zero To Mastery (ZTM) is a Toronto-based online coding academy founded by Andrei Neagoie, originally a senior developer at large Canadian tech firms before turning to teaching full-time. The academy's signature is the cohort-based bootcamp track combined with a deep self-paced course library, all aimed at career-changers and self-taught developers preparing to land software-engineering roles at top companies.

The instructor roster has grown well beyond Andrei to include other senior practitioners: Daniel Bourke (machine learning), Aleksa Tešić (DevOps), Jacinto Wong, and others. Courses cover the full software-engineering career path: web development with React and Next.js, Python, machine learning and deep learning, DevOps and cloud, system design, mobile, and the algorithm / data-structure interview prep that gates engineering jobs.

The CourseFlix listing under this source carries over 120 ZTM courses spanning that full range. Material is paid; ZTM itself runs on a monthly / annual membership model. The teaching style favours long-form, project-based courses where students build complete portfolio-quality applications rather than disconnected feature tutorials.

What lessons are included in Local AI Revolution: Ollama and OpenClaw?

This is a demo lesson (10:00 remaining)

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#1: Introduction to the Local AI Revolution
All Course Lessons (24)
#Lesson TitleDurationAccess
1
Introduction to the Local AI Revolution Demo
04:35
2
DigitalOcean Sign Up and SSH Setup
04:09
3
Create the Droplet and Set Up Your User
08:40
4
Install and Configure Ollama
07:05
5
Where to Find Ollama Models: Ollama Library, Hugging Face, GGUF, and Model Tags
06:39
6
Pull Models and Run Your First Inference
11:39
7
Parameters, Quantization, and VRAM: Why Models Are the Size They Are
11:59
8
Adjusting Model Behaviour in the Interactive Shell
07:20
9
Saving and Reusing Model Configurations
06:32
10
Customizing Models with Modelfile
07:55
11
ChatGPT-Like Interface for Your Private AI: Open WebUI Setup
07:20
12
Access Your AI Securely From Anywhere: SSH Tunneling Explained
08:14
13
LM Studio: A Desktop Alternative to Ollama
05:19
14
Introduction: From CPU to GPU
03:35
15
Sign Up on Vast.ai and Add Your SSH Key
04:44
16
Deploy a GPU Instance and Connect
07:24
17
Install and Configure Ollama
07:27
18
Pull Models and Manage the Instance
07:16
19
CPU vs GPU - Live Benchmark
05:14
20
What Is Agentic AI? The Shift From Chatbots to Autonomous Agents
08:22
21
Install and Configure OpenClaw
06:39
22
Session Startup and Terminal Multiplexing
11:26
23
Connect Telegram to OpenClaw
08:22
24
OpenClaw Config: Telegram Hardening
04:03
Watch the other 23 lessons

One payment unlocks this course and every other premium course. No auto-renew.

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Frequently asked questions

Can I follow this if I've never set up a local AI model before?
Yes—the course starts with server setup and your first model installation, so previous experience running local models isn't expected. You'll work with a Linux server, SSH connections, and terminal commands rather than staying entirely inside a desktop app. The setup is taught step by step, but expect to spend some time getting comfortable with that workflow. No specific programming background or minimum hardware requirements are stated.
What will I actually set up beyond a chatbot?
You'll set up Ollama to run language models, then add Open WebUI so you can interact with them through a browser-based chat interface. You'll also work with model settings and reusable configurations rather than just accepting the defaults. The later section moves into OpenClaw, where you'll configure an agent and connect it to Telegram. That gives you practice with both a model-serving setup and an agent you can communicate with.
Does 'local AI' mean everything runs on my own computer?
Not necessarily. Here, local AI means running models in an environment you manage rather than relying on an external model API. Much of the practical setup uses a Linux server: you'll work with DigitalOcean for the initial deployment and Vast.ai for a rented GPU instance. There's also a look at LM Studio as a desktop alternative. If your goal is a strictly offline, laptop-only setup, that isn't the main workflow.
Will I learn how to choose and use a GPU for larger models?
You'll get practical guidance on moving model workloads from CPU to GPU, including renting a GPU server and connecting to it. The course also explains how model parameters, quantization, and VRAM relate to model size, which helps you understand resource demands. A live CPU-versus-GPU benchmark gives you a concrete comparison. Expect deployment and resource-management practice, rather than a deep dive into GPU architecture or low-level performance engineering.
Is this about training my own AI, or running models that already exist?
The focus is running and configuring existing language models. You'll learn where to find models, pull them into Ollama, adjust their behavior, and save configurations with a Modelfile. That customization isn't the same as training a model or fine-tuning its weights. If you're mainly looking for dataset preparation, training pipelines, or model-development theory, those aren't established parts of this course. The emphasis is getting models and an agent system operational.
How much time should I set aside to work through it?
The videos total 2 hours and 51 minutes across 24 lessons. If you're following along, allow extra time for account setup, server deployment, model downloads, and troubleshooting your own environment. Those tasks won't take the same amount of time for everyone, so the video runtime isn't a reliable estimate of your total commitment. You can treat the initial server setup and the later GPU deployment as separate practice sessions.
What skills can I reuse if I switch models or tools later?
You'll practice SSH access, Linux server setup, model selection, and thinking about the relationship between model size and available memory. Those skills are useful beyond a single model or chat interface. You'll also work with saved model configurations, secure remote access through SSH tunneling, and the distinction between a chatbot and an autonomous agent. Exact commands will vary between tools, but you'll have practical experience with the underlying deployment decisions.