Ollama & OpenClaw: Run Open Models on Your Own Stack

About this course

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.

Set up a Linux server, configure SSH, and create a secure user

Install and configure Ollama to serve open weight models via APIPull models from the Ollama library, Hugging Face, and GGUF sources

Understand quantization, VRAM requirements, and how to pick the right model size

Control model behaviour: temperature, context length, and runtime parameters

Build custom model variants using Ollama Modelfiles

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