Local AI: Run Models on Your Hardware
Run AI models on your own machine — zero cloud costs, complete privacy. Set up Ollama, fine-tune models on your data, build private AI systems that never leave your hardware.
Courses are not sold one at a time. One subscription opens this curriculum and every other one, each with its own coach. See the full terms. Card required to start the trial; the first payment lands on day 8 and renews until you cancel.
What's Included
- The full curriculum — 5 modules, 32 lessons
- An AI coach that knows this curriculum and your business
- Progress tracking across the curriculum
No videos, no written lesson pages, no quizzes, no certificate. You work through the curriculum in conversation with the coach.
7-Day Money-Back Guarantee
Not satisfied? Get a full refund within 7 days. No questions asked.
What You'll Learn
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
- -GPU with 8GB+ VRAM recommended (course covers CPU-only options too)
- -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
Module 5: Local AI Projects
- 5.1Build a local chatbot with a custom system prompt and memory
- 5.2Offline document assistant: summarize, extract, and query your files
- 5.3Local code helper: code review and generation without cloud APIs
- 5.4Private email drafter: compose and rewrite emails locally
- 5.5Local meeting summarizer: transcribe and summarize audio files
- 5.6Your Local AI Setup: Benchmark, Document, and Share Your Configuration
Ready to Start Learning?
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