Running AI Locally
Deploy, optimize, and build with open-weight language models entirely on your own hardware. Learn how quantization, memory bandwidth, and lightweight runtimes enable private, low-latency AI workflows without reliance on cloud APIs.
Like Duolingo, but for Running AI Locally. Tomo turns the whole topic into a game you play five minutes a day, until it actually sticks.
21 levels across 3 sections, about 42 minutes end to end, roughly 8 days at five minutes a day. It moves through Executing Local Inference, Quantization and Runtime Tuning, and Local Applications and Customization. It assumes you already know the basics.
Free forever · No credit card · iPhone & Android
Key ideas in Running AI Locally
- Local inference incurs zero per-token API costs after hardware purchase.
- On-premise execution guarantees sensitive user data never leaves the local machine.
- Local models operate without internet access but are strictly bounded by host hardware.
- Tools like Ollama and LM Studio wrap high-performance C++ engines like llama.cpp.
- Desktop runtimes automate downloading weights, quantization profiles, and background servers.
- The underlying C++ runtime handles hardware acceleration and raw tensor operations.
- GGUF is the standard single-file distribution format used by llama.cpp and Ollama.
- A GGUF file bundles quantized tensor weights, tokenizer vocabulary, and architecture metadata.
- Single-file distribution prevents version mismatches between tokenizers and model weights.
- Local token generation rate is primarily bottlenecked by memory bandwidth.
- Time-to-first-token locally depends on compute throughput processing prompt context.
- Cloud API latency is heavily influenced by physical network distance and queue times.
- Local inference daemons expose HTTP endpoints mimicking OpenAI's /v1/chat/completions route.
- Existing developer SDKs switch from cloud to local by redirecting their base URL parameter.
- OpenAI client libraries accept arbitrary API keys when querying local loopback addresses.
- Generating each token requires reading the entire model's weights from memory once
You've tried the other tabs
Thirty open tabs. Four facts you actually kept.
You watched. You nodded. By Sunday it was gone.
One answer, then back to scrolling.
Eight weeks. You meant to finish. You didn't.
Tomo gives Running AI Locally the Duolingo treatment: levels, streaks, and quick quizzes that test what you just learned. That game loop is what the tabs above never had, so it's the one you actually finish.
Here's what playing it feels like
A real question from this course. Take your best guess.
What happens when you run a local AI without an internet connection?
Get it right to open this lesson and 20 more in the app.
Where Running AI Locally takes you
- 1
Executing Local Inference
- The First Local Inference Run
- Sizing Models to Hardware Constraints
- 2
Quantization and Runtime Tuning
- Model Quantization and Compression Formats
- Tuning Inference Engine Performance
- 3
Local Applications and Customization
- Building Offline Retrieval-Augmented Generation
- Structured Outputs and Local Agent Loops
- Consumer-Grade Fine-Tuning with LoRA
3 sections · 7 units · 21 levels. Built to play, not to enroll.
Tomo is in ChatGPT
Tomo is also a ChatGPT plugin: the same playable courses, built and played inside your chat.
Add Tomo to ChatGPT- What to ask
- “Make me a Tomo course on black holes.” “Turn this chat into a Tomo course.”
- What you get
- Quick questions with instant feedback, hearts and stars, ready to play in about a minute.
- Where it goes
- No sign-up to start. Connect your Tomo account and your streak carries on in the app.
You pick the voice
Running AI Locally is taught in the Explain Like I'm 5 style: no big words. promise.. Want a different feel? In the app you can spin up the same topic in any of Tomo's teaching styles. Same facts, totally different vibe.
More Technology on Tomo
Working with Modern AI
Understand how contemporary artificial intelligence models actually process, reason, and generate information. Learn to steer models via structured outputs, ground them with retrieval systems, and navigate the architectural trade-offs powering modern AI applications.
Building AI Applications
Bridge the gap between raw LLM API calls and robust, production-ready software. Learn the patterns for reliable structured outputs, grounded retrieval pipelines, tool orchestration, and automated evaluation.
The Modern AI Stack
Move beyond basic prompt engineering to understand how cutting-edge AI systems actually operate. Explore the architectural shifts powering retrieval, tool-calling agents, inference scaling, and multi-step autonomy.
Practical AI and Prompt Workflows
Move beyond conversational chat to use language models as analytical engines and structured workflow partners. Learn prompt architecture, token mechanics, schema extraction, and tool integration.
AI-Native Software Engineering
Transition from using AI as a chatbot to integrating it as a core architectural component. This course covers advanced agentic workflows, automated evaluation loops, and the shift toward non-deterministic system design.
Transformer Architectures and the Future of LLMs
Master the intricate mechanics of modern large language models and explore the frontier of post-transformer architectures, from State Space Models to neuro-symbolic reasoning.
Start Running AI Locally today.
Download Tomo, search Running AI Locally, and play your first lesson in under a minute.