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DevelopmentIntermediate27 lessons10–14 hours
Local AI: Run Models on Your Hardware
Run powerful AI models on your own hardware. Ollama, llama.cpp, local fine-tuning, and building private AI systems that never send data to the cloud.
What You'll Learn
Install and run open-source models locally with Ollama
Understand quantization formats (GGUF, GPTQ, AWQ) and their tradeoffs
Choose the right hardware: GPU memory, CPU inference, and Apple Silicon
Fine-tune models on your own data with LoRA and QLoRA
Build private AI assistants that keep all data on your machine
Set up offline RAG systems with local embeddings and vector stores
Optimize GPU utilization for faster inference and lower memory usage
Compare total cost of ownership: cloud APIs vs local hardware
Outcomes
- Run open-source AI models locally with zero cloud costs
- Build private AI systems that keep all data on your hardware
- Fine-tune models for your specific use case
- Set up local RAG and application pipelines
Prerequisites
- -Command line basics
- -8GB+ RAM recommended
- -Basic Python helpful
Projects You'll Build
- Set up a local AI development environment with Ollama
- Build a private document Q&A system
- Fine-tune a model on your own data
Course Curriculum
Module 1: Getting Started with Ollama
- 1.1Why run AI locally: privacy, cost, speed, and control
- 1.2Installing Ollama on macOS, Windows, and Linux
- 1.3Downloading and running your first model (Llama 3, Mistral, Gemma)
- 1.4The Ollama CLI: pull, run, list, remove, and model management
- 1.5Ollama API: integrating local models into your applications
- 1.6Open WebUI: a ChatGPT-like interface for local models
- 1.7Model comparison: Llama 3 vs Mistral vs Phi vs Gemma
Module 2: Model Management & Optimization
- 2.1Understanding quantization: Q4, Q5, Q8, and full precision
- 2.2GGUF format deep dive: how llama.cpp powers local inference
- 2.3Hardware requirements: what you can run on 8GB, 16GB, 24GB, and 48GB+ VRAM
- 2.4CPU vs GPU inference: when each makes sense
- 2.5Apple Silicon optimization: Metal and unified memory advantages
- 2.6Context length management: running models with larger context windows
- 2.7Batching and concurrent requests for local model servers
Module 3: Local RAG & Applications
- 3.1Local embedding models: nomic-embed, mxbai-embed, all-MiniLM
- 3.2Setting up ChromaDB or LanceDB for local vector storage
- 3.3Building a private document Q&A system entirely offline
- 3.4Local AI coding assistant with Continue and Ollama
- 3.5Private note-taking with AI summarization and search
- 3.6Offline translation and multilingual applications
Module 4: Fine-Tuning & Advanced Topics
- 4.1When to fine-tune vs when to use prompting and RAG
- 4.2LoRA and QLoRA: efficient fine-tuning on consumer hardware
- 4.3Preparing training data: format, quality, and size guidelines
- 4.4Fine-tuning with Unsloth for 2x speed and half the memory
- 4.5Evaluating your fine-tuned model against the base
- 4.6Converting and exporting models to GGUF for Ollama
- 4.7The future of local AI: smaller models, faster hardware, and on-device inference
AI isn't slowing down.
Neither should you.
Every week you wait, the gap widens. The people who invest in learning AI now will be the ones leading teams, building companies, and staying ahead of the curve. This is your moment — don't let it pass.