Course · 7 chapters
Ejecuta un LLM en tu propia máquina
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
- 1Ejecuta un LLM en tu propia máquina: empieza aquí
El mapa de este path — por qué vale la pena ejecutar modelos en local, cómo los seis capítulos se construyen unos sobre otros, y por dónde empezar.
- 2Por qué ejecutar un LLM en local
El modelo en tu propia máquina es más débil que la frontera — y vale la pena hacer ese intercambio más a menudo de lo que crees.
- 3Las herramientas que ejecutan modelos
Cuatro puertas de entrada amables, un mismo motor compartido debajo — así que la elección va de comodidad, no de capacidad.
- 4La realidad del hardware y la quantization
Un solo número decide si un modelo corre en tu máquina — y un truco te deja doblegarlo.
- 5Elige tu modelo local
Tu hardware ya estrechó el campo — ahora elige por la tarea y lee la licencia antes de comprometerte.
- 6Sirve un modelo a tu app
Los modelos locales hablan el mismo idioma que la nube — así que conectar uno a tu código es, en su mayor parte, cambiar una sola línea.
- 7Construye una mejor máquina doméstica
¿Se te quedó pequeño el hardware? La mejora es un solo número — haz crecer el tipo de memoria adecuado, y se abre toda una clase de modelos.
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.