Udemy – LLM Crash Course: Run Models Locally. Master LLM Engineering 2025-6

Udemy – LLM Crash Course: Run Models Locally. Master LLM Engineering 2025-6 Downloadly IRSpace

Aug 17, 2025 - 02:42 Updated: Aug 19, 2025 - 14:37

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Udemy – LLM Crash Course: Run Models Locally. Master LLM Engineering 2025-6

LLM Crash Course: Run Models Locally. Master LLM Engineering is a course on understanding and deploying large language models (LLM) on local devices published by Udemy Online Academy. This is a crash course designed to help learners understand and deploy large language models (LLM) on local devices. The course demystifies the complexities of LLM by covering fundamental concepts, practical setup, optimization techniques, and practical engineering workflows. Individuals gain the skills needed to efficiently run, fine-tune, and integrate LLMs into applications without relying solely on cloud services, enabling them to build scalable and customizable AI solutions while maintaining data privacy and control.

This course covers the fundamentals of large language models and their architectures, setting up local environments to effectively run LLMs, managing hardware and software requirements, fine-tuning and optimizing models for specific tasks, integrating LLMs into applications and workflows, troubleshooting common problems, applying engineering best practices for performance and scalability, understanding ethical and data privacy considerations, and preparing for real-world deployment scenarios in LLM engineering.

What you will learn in LLM Crash Course: Run Models Locally. Master LLM Engineering:

Set up and run open-source LLMs locally on Windows, macOS, or Linux without the cloud, without API keys, and without recurring fees.
Install and configure tools like Python, Poetry, and model runtimes to deploy and manage LLMs in a completely offline environment
Use Python to send notifications and receive responses from local LLMs, simulating real conversations through structured message streams.
Understand LLM roles, token constraints, context windows, stream responses, and notification design for better model control.
Build LLM tools like a customer support agent using function calls and structured responses.
Create a simple Retrieval-Augmented Generation (RAG) program to enhance your LLM with external data for better context-aware outputs.
And…

Course specifications

Publisher: Udemy
Instructors: Ganesh S
Language: English
Level: Introductory to Advanced
Number of Lessons: 16
Duration: 3 hours and 48 minutes

Course topics

LLM Crash Course Run Models Locally. Master LLM Engineering Content

LLM Crash Course: Run Models Locally. Master LLM Engineering Prerequisites

Knowledge of Python programming — you should know how to install python, write, debug and run Python scripts.
Familiarity with the command line or terminal — you’ll use basic commands during setup and testing.
A computer running Windows, macOS, or Linux — the course includes platform-specific setup instructions.
At least 8GB of RAM (16GB recommended) — running language models locally requires a reasonable amount of memory.
A stable internet connection for the initial setup only — after that, everything runs 100% offline.
No prior experience with LLMs, AI, or machine learning is required — all key concepts are explained with hands-on examples.