Ollama & OpenClaw: Run Open Models on Your Own Stack | Udemy [Update 06/2026]
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Genre: eLearning[/center]

Deploy self-hosted LLMs for your team: Ollama, OpenClaw, GPU inference - no vendor lock-in, no data leaving your servers

What you'll learn:
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[*]Run open-weight AI models privately using Ollama on a cloud VPS and GPU instance - no API key, no subscription, no data leaving your server
[*]Set up Open WebUI for a private, ChatGPT-like interface connected directly to your own locally running models
[*]Deploy autonomous AI agents using OpenClaw - web search, file access, and multi-step task execution on your own infrastructure
[*]Run live CPU vs GPU benchmarks and understand why VRAM matters for LLM inference speed
[*]Connect to your private AI stack securely from anywhere using SSH tunneling
[*]Manage open-weight models, Modelfiles, and session configurations to control model behavior in real deployments
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Run open source LLMs on infrastructure you own - no API bills, no data leaving your servers.
This is the most complete hands-on course for running Ollama and OpenClaw on your own private infrastructure. You will learn to deploy open weight models on a Linux server and a rented GPU, build a self-hosted AI assistant with Open WebUI, and connect an autonomous AI agent through OpenClaw - all without sending a single token to a third-party API.
If you are a developer tired of API costs, worried about data privacy, or building AI tools for a team or a client, this course gives you a complete, working stack you control from day one.
What makes this course different
Most Ollama courses run models on a laptop. Most OpenClaw courses wire it to cloud APIs. This course does neither. You will rent a server, configure it from scratch, install and serve Ollama, pull open weight models, and connect OpenClaw as an autonomous agent - all on infrastructure you fully control. You will also deploy a GPU instance on a cloud GPU platform and run a live benchmark showing the real speed difference between CPU and GPU inference: over 60 times faster, at a fraction of the cost of a dedicated machine.
Section 1 - Ollama on a Private Server
You start with a fresh Linux VPS and end with a fully working private AI stack.
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[*]Set up a Linux server, configure SSH, and create a secure user
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[*]Install and configure Ollama to serve open weight models via API
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[*]Pull models from the Ollama library, Hugging Face, and GGUF sources
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[*]Understand quantization, VRAM requirements, and how to pick the right model size
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[*]Control model behaviour: temperature, context length, and runtime parameters
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[*]Build custom model variants using Ollama Modelfiles
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[*]Deploy Open WebUI - a self-hosted ChatGPT-style interface for your models
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[*]Access your private AI securely from anywhere using SSH tunneling
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[*]Explore LM Studio as a desktop-based alternative to Ollama
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Section 2 - GPU Inference and OpenClaw
You take the same stack to a rented GPU and add an autonomous AI agent.
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[*]Deploy a GPU instance on a cloud GPU platform from scratch
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[*]Install Ollama on the GPU instance and serve open weight models at full speed
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[*]Run a live CPU vs GPU benchmark: real numbers, same model, same prompt
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[*]Learn what agentic AI is and how it differs from a chatbot or a RAG pipeline
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[*]Install and configure OpenClaw on your own server
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[*]Connect Telegram as an interface for your OpenClaw agent
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[*]Manage persistent terminal sessions with tmux for always-on agent operation
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[*]Harden your OpenClaw configuration for secure, production-ready deployment
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Who this course is for
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[*]Developers who want to run open source models locally or on a private server
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[*]Teams that cannot send data to external APIs due to privacy or compliance requirements
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[*]Engineers exploring agentic AI with OpenClaw and local LLMs
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[*]Anyone paying monthly AI API bills who wants a cost-effective self-hosted alternative
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[*]Developers curious about Ollama
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, Open WebUI, GPU inference, and autonomous agents
Tools and stack covered
Ollama - OpenClaw - Open WebUI - GPU cloud - Linux VPS - LM Studio - tmux - SSH tunneling - Ollama Modelfiles - GGUF - Hugging Face - Telegram
What you will be able to do after this course
By the end, you will have a fully working self-hosted AI stack: Ollama serving open weight models on both a CPU server and a GPU instance, Open WebUI as a private chat interface, and OpenClaw running as an autonomous agent accessible via Telegram - all on infrastructure you rent, control, and can shut down whenever you want.
No vendor lock-in. No API subscriptions. No data leaving your infrastructure.
If you want to run powerful open weight models privately, build with OpenClaw, and own the infrastructure under your AI stack - this course is the fastest path to get there.

Who this course is for:
Developers who want to run AI models on their own infrastructure - no API key, no monthly subscription, no data sent to third-party servers
Developers who want to build and run autonomous AI agents on self-hosted open-source models using a GPU cloud instance
Engineers who work with sensitive code or client data and need a private AI assistant that stays on their own machine or server
Anyone interested in running the latest open-weight AI models on their own hardware - Mac, Linux box, compact desktop, or rented cloud GPU

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