Ollama & OpenClaw: Run Open Models on Your Own Stack

<div>Run open source LLMs on infrastructure you own — no API bills, no data leaving your servers.</div><div><br></div><div>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.</div><div><br></div><div>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.</div><div><br></div><div>What makes this course different</div><div><br></div><div>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.</div><div><br></div><div>Section 1 — Ollama on a Private Server</div><div><br></div><div>You start with a fresh Linux VPS and end with a fully working private AI stack.</div><div><ul><li>Set up a Linux server, configure SSH, and create a secure user</li><li><span style="font-size: 1rem;">Install and configure Ollama to serve open weight models via API</span></li><li><span style="font-size: 1rem;">Pull models from the Ollama library, Hugging Face, and GGUF sources</span></li><li><span style="font-size: 1rem;">Understand quantization, VRAM requirements, and how to pick the right model size</span></li><li><span style="font-size: 1rem;">Control model behaviour: temperature, context length, and runtime parameters</span></li><li><span style="font-size: 1rem;">Build custom model variants using Ollama Modelfiles</span></li><li><span style="font-size: 1rem;">Deploy Open WebUI — a self-hosted ChatGPT-style interface for your models</span></li><li><span style="font-size: 1rem;">Access your private AI securely from anywhere using SSH tunneling</span></li><li><span style="font-size: 1rem;">Explore LM Studio as a desktop-based alternative to Ollama</span></li></ul></div><div><span style="font-size: 1rem;">Section 2 — GPU Inference and OpenClaw</span></div><div><br></div><div>You take the same stack to a rented GPU and add an autonomous AI agent.</div><div><ul><li>Deploy a GPU instance on a cloud GPU platform from scratch</li><li><span style="font-size: 1rem;">Install Ollama on the GPU instance and serve open weight models at full speed</span></li><li><span style="font-size: 1rem;">Run a live CPU vs GPU benchmark: real numbers, same model, same prompt</span></li><li><span style="font-size: 1rem;">Learn what agentic AI is and how it differs from a chatbot or a RAG pipeline</span></li><li><span style="font-size: 1rem;">Install and configure OpenClaw on your own server</span></li><li><span style="font-size: 1rem;">Connect Telegram as an interface for your OpenClaw agent</span></li><li><span style="font-size: 1rem;">Manage persistent terminal sessions with tmux for always-on agent operation</span></li><li><span style="font-size: 1rem;">Harden your OpenClaw configuration for secure, production-ready deployment</span></li></ul></div><div><span style="font-size: 1rem;">Who this course is for</span></div><div><ul><li>Developers who want to run open source models locally or on a private server</li><li><span style="font-size: 1rem;">Teams that cannot send data to external APIs due to privacy or compliance requirements</span></li><li><span style="font-size: 1rem;">Engineers exploring agentic AI with OpenClaw and local LLMs</span></li><li><span style="font-size: 1rem;">Anyone paying monthly AI API bills who wants a cost-effective self-hosted alternative</span></li><li><span style="font-size: 1rem;">Developers curious about Ollama, Open WebUI, GPU inference, and autonomous agents</span></li></ul></div><div><span style="font-size: 1rem;">Tools and stack covered</span></div><div><br></div><div>Ollama — OpenClaw — Open WebUI — GPU cloud — Linux VPS — LM Studio — tmux — SSH tunneling — Ollama Modelfiles — GGUF — Hugging Face — Telegram</div><div><br></div><div>What you will be able to do after this course</div><div><br></div><div>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.</div><div><br></div><div>No vendor lock-in. No API subscriptions. No data leaving your infrastructure.</div><div><br></div><div>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.</div>

What you'll learn:

• 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