Udemy — Local AI: Run Open Models on Your Own Machine 2026-10

Description

Dr. Amar Massoud introduces the practical decisions involved in running open models on a personal computer. Model cards, parameter counts, quantization and file formats are considered alongside CPU, GPU, RAM and storage constraints. The course sets up local inference and an OpenAI-compatible endpoint that other scripts and tools can call.

A repeatable file-processing workflow moves beyond chat to produce a saved output from a folder of documents. Local and cloud approaches are compared through evaluation, followed by a hybrid design that includes human review. The business-oriented section considers possible applications and customer niches.

Intended audience: Developers, technology professionals and AI enthusiasts exploring local inference.

What you will learn

• Select model configurations with hardware limits in mind.
• Run local inference and expose a compatible API endpoint.
• Build a repeatable document-processing workflow.
• Evaluate local, cloud and hybrid application designs.

Course specifications

Publisher

Udemy

Instructors

Dr. Amar Massoud

Language

English

Level

Beginner to Advanced

Duration

3 hours and 24 minutes

Lecture count

40

Quality

1080p

Edition

2026-10

Course topics

View course topics

Prerequisites

• A Mac, Windows or Linux computer with at least 8 GB RAM; 16 GB is recommended.
• Internet access for obtaining the models; no coding background is assumed.

Introduction video

Download resources

Choose one edition and keep its parts together. Alternate providers are available when listed.

Size

2.4 GB

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