NextGen AI
Intermediate31% off

Local LLMs: Run Models on Your Own Hardware

Run powerful LLMs on your own hardware. Gain control, privacy, and cost-efficiency for your AI projects.

4.8
(5,100)
5,100 studentsEnglish

Created by Alex Rivera · Full-Stack AI Engineer

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This course includes

  • 15 video lessons
  • 4h 14m of content
  • Downloadable resources
  • Full lifetime access
  • Certificate of completion
  • 30-day money-back guarantee

What you'll learn

  • Understand the benefits and trade-offs of running LLMs locally.
  • Identify suitable hardware (CPU, GPU, RAM) for local LLM inference.
  • Explore and select from popular open-source LLMs (Llama 3, Mistral, Phi-3).
  • Set up and use inference engines like Ollama and LM Studio.
  • Master techniques for model quantization to reduce resource requirements.
  • Integrate local LLMs into Python applications using libraries like LangChain.
  • Optimize inference speed and resource utilization.
  • Manage multiple local models effectively.

About this course

Cloud-based LLM APIs are convenient, but they come with costs, privacy concerns, and vendor lock-in. Running Large Language Models (LLMs) locally offers unparalleled control, enhanced data privacy, and significant cost savings, especially for frequent or sensitive workloads. This course demystifies the process, making powerful AI accessible on your own machines. We'll guide you through selecting the right hardware, navigating the landscape of open-source models (like Llama 3, Mistral, Phi-3), and setting up the necessary software frameworks (Ollama, LM Studio, llama.cpp). You'll learn practical techniques for quantization, model management, and efficient inference, allowing you to deploy sophisticated LLMs without relying on external services. Whether you're a researcher needing offline access, a developer prioritizing data security, or an enthusiast exploring AI's cutting edge, this intermediate course provides the essential knowledge and hands-on skills to bring LLMs home.

Course content

5 modules · 15 lessons · 4h 14m

  • Why Run LLMs Locally?Preview
    16m
  • Hardware Requirements: CPU, GPU, and RAMPreview
    20m
  • Understanding Model Sizes and Parameters
    14m

Requirements

  • Comfortable using the command line interface (CLI).
  • Basic understanding of AI/ML concepts.
  • Familiarity with Python is helpful but not strictly required for initial setup.

Your instructor

A

Alex Rivera

Full-Stack AI Engineer

Alex has built and launched a dozen AI products and teaches developers to ship with LLMs the pragmatic way.