Udemy – Local LLMs via Ollama & LM Studio – The Practical Guide 2025-4

Udemy – Local LLMs via Ollama & LM Studio – The Practical Guide 2025-4 Downloadly IRSpace

Aug 16, 2025 - 06:57 Updated: Aug 17, 2025 - 19:33

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Udemy – Local LLMs via Ollama & LM Studio – The Practical Guide 2025-4

Local LLMs via Ollama & LM Studio – The Practical Guide. In today’s world of cloud-based AI and chatbots like ChatGPT, running advanced models locally offers significant benefits. Imagine getting advanced AI at zero or very low cost (free access to powerful models), complete privacy (your data and requests always remain safe on your device), offline functionality (access to powerful AI tools anytime, anywhere), freedom from being tied to a specific company (access to a diverse and growing ecosystem of open source models), and amazing capabilities (open source models like Gemma, Llama, and DeepSeek are not only alternatives, but also perform very well in Chatbot Arena benchmarks and rankings).

This course is a comprehensive and practical journey into the world of local LLMs. It takes you step-by-step from basic setup to advanced usage, cutting through the complexities. In this course, participants will be introduced to the landscape of open source large language models, understanding what they are, why they matter, and where to find them. It will also fully explain the real-world hardware requirements for running LLMs locally. The concept of quantization, a technique that enables large models to be run on off-the-shelf hardware, is explained in detail.

What you will learn

Explore and understand the use cases of Open-Source Large Language Models (Open-LLM).
Achieve 100% privacy and control by running powerful open source large language models locally.
Selecting and implementing large open source language models such as Gemma 3 or Llama 4.
Using Ollama and LM Studio to run large open source language models locally.
Analyze text, documents, and images using open source large language models.
Integrate large, open-source language models running locally into custom AI-based applications and applications.