Ollama & OpenClaw: Run Open Models on Your Own Stack

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 API

• Pull 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

• Deploy Open WebUI — a self-hosted ChatGPT-style interface for your models

• Access your private AI securely from anywhere using SSH tunneling

• Explore LM Studio as a desktop-based alternative to Ollama

Section 2 — GPU Inference and OpenClaw

You take the same stack to a rented GPU and add an autonomous AI agent.

• Deploy a GPU instance on a cloud GPU platform from scratch

• Install Ollama on the GPU instance and serve open weight models at full speed

• Run a live CPU vs GPU benchmark: real numbers, same model, same prompt

• Learn what agentic AI is and how it differs from a chatbot or a RAG pipeline

• Install and configure OpenClaw on your own server

• Connect Telegram as an interface for your OpenClaw agent

• Manage persistent terminal sessions with tmux for always-on agent operation

• Harden your OpenClaw configuration for secure, production-ready deployment

Who this course is for

• Developers who want to run open source models locally or on a private server

• Teams that cannot send data to external APIs due to privacy or compliance requirements

• Engineers exploring agentic AI with OpenClaw and local LLMs

• Anyone paying monthly AI API bills who wants a cost-effective self-hosted alternative

• Developers curious about Ollama, 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.