Course · 7 chapters
Run an LLM on Your Own Machine
Run a capable private LLM on the laptop you already own, no cloud bill. Pick a tool, fit a model to your VRAM with quantization, then serve it to your app. 7 chapters, ~2h.
What you'll be able to do
- Run an LLM on your own machine
- Pick the right local model for the job
- Read VRAM and quantization specs
- Serve a model over an OpenAI API
- Weigh local models against cloud models
- Spec hardware to run bigger models
What's inside
- 1Run an LLM on Your Own Machine: Start Here
The map of this path — why running models locally is worth it, how the six chapters build on each other, and where to begin.
- 2Why Run an LLM Locally
The model on your own machine is weaker than the frontier — and that trade is worth making more often than you'd think.
- 3The Tools That Run Models
Four friendly front doors, one shared engine underneath — so the choice is about comfort, not capability.
- 4Hardware & Quantization Reality
One number decides whether a model runs on your machine — and one trick lets you bend it.
- 5Pick Your Local Model
Your hardware already narrowed the field — now choose by the job and read the license before you commit.
- 6Serve a Model to Your App
Local models speak the same language as the cloud — so wiring one into your code is mostly changing a single line.
- 7Build a Better Home Machine
Outgrew your hardware? The upgrade is one number — grow the right kind of memory, and a whole model class opens up.
Frequently asked questions
- What will I learn in this course?
- You learn to run a large language model on the computer you already own: choosing a tool to run models, fitting a model to your hardware using VRAM and quantization, picking a model by task and license, serving it to your own app, and upgrading your machine when you outgrow it. The path runs across 7 chapters covering tools, hardware, model choice, and serving.
- Who is this course for?
- It is built for engineers and curious beginners who want a local LLM running on their own laptop or PC instead of relying only on cloud APIs. No prior experience with local models is assumed, since the path starts from why local matters and builds up step by step.
- Do I need to code or have machine learning experience?
- You do not need any machine learning background. Basic comfort with a terminal helps for the chapter on serving a model to your app, where you connect it through an OpenAI-compatible API, but the path is written for non-experts.
- How long is the course and is there a certificate?
- The path is about 2 hours total across 7 chapters at a foundations level. You can complete it at your own pace and earn a certificate of completion in the AI Academy.
- Is this course free?
- No, this is a paid path included with an AI Academy subscription. It covers running a local LLM end to end, from choosing a tool to serving a model and upgrading your hardware.
Earn a certificate
Complete all chapters to receive your certificate of completion.