Learn to deploy large language models directly on your local hardware without relying on cloud services. This tutorial guides you through setting up Ollama, understanding model quantisation, and optimising LLMs to run efficiently on consumer-grade GPUs. You'll gain practical knowledge of edge deployment whilst maintaining complete data privacy and offline capability. Discover how quantised models reduce memory footprint and computational overhead, making sophisticated AI accessible on modest hardware. By the end of this course, you'll have built a fully functional private AI assistant that processes sensitive information locally, ensuring your data never leaves your device. Perfect for developers, data scientists, and AI enthusiasts who value privacy and want to explore the cutting edge of edge computing without cloud dependencies.

Lessons

  1. Introduction to Local LLM Deployment — Understanding edge computing, privacy benefits, and when to deploy locally vs. cloud (+75 XP)
  2. Setting Up Ollama and Your First Model — Install Ollama, configure your environment, and run your first open-source language model (+100 XP)
  3. Model Quantisation Fundamentals — Learn how quantisation reduces model size, memory usage, and computational requirements (+100 XP)
  4. Selecting and Optimising Models for Your Hardware — Match models to your device specs, benchmark performance, and optimise inference speed (+125 XP)
  5. Building a Private AI Assistant — Create a conversational interface that integrates Ollama with Python for real-world use cases (+150 XP)
  6. Performance Tuning and Monitoring — Measure latency, memory usage, and throughput; implement optimisation strategies (+125 XP)
  7. Production Deployment and Security Best Practices — Secure your local deployment, manage multiple models, and prepare for real-world scenarios (+150 XP)