What is local AI?

Local AI is AI that runs entirely on your own device: the model files live on your computer and the computation happens on your chip, with nothing sent to a server. No round-trip to a data center, no account, no per-message quota. You type a question, your laptop does the math, and the answer appears. The same way a calculator works: the machine in front of you does the thinking.

How is local AI different from cloud AI?

It comes down to where the model lives and where the computing happens. Cloud AI (ChatGPT, Claude, Gemini) keeps the model on a company's servers; you send text up, they run it, they send an answer back. Local AI keeps both the model and the work on your machine. That one difference cascades into everything else: your data, your internet dependency, your bill.

Why do people use local AI?

Five reasons keep showing up, and most of them got louder in 2026:

• Privacy. Your prompts and files stay on your disk. Nothing is logged on a server or used to train a future model. That matters when the work is a client contract, a medical note, or unreleased code. See why local AI keeps code private.
• No usage caps. There's no meter to hit. People have literally written about scheduling their day around cloud limits; a popular Hacker News thread is titled "Optimizing my sleep around Claude usage limits." A local model just runs.
• Offline. It works on a plane, in a basement, in a country with bad Wi-Fi. The model is already on your machine, so there's nothing to connect to.
• Ownership. The weights are files you keep. They can't be deprecated, repriced, or quietly changed mid-project, unlike GPT-4, which OpenAI removed from ChatGPT in April 2025.
• Cost. Subscriptions stack up and prices are moving. Axios ran a piece called "AI sticker shock" in May 2026, and "Corporate America Is Starting to Ration AI as Cost Skyrockets" landed on May 30. A model you already have costs only the electricity to run it.

What do you need to run local AI?

Two things: a capable chip and enough memory. Apple Silicon Macs (the M1 chip from 2020 or newer) are well suited because the chip and memory share one fast pool, which is exactly what running a model wants. The size of model you can run scales with RAM: 16 GB handles small, fast models comfortably, and more RAM opens up bigger ones. If you want the deeper version, our Apple Silicon guide walks through chips and memory.

RAM is usually the hard ceiling: a model has to fit in memory to run, and the best models are large. Outlier works around that with a patent-pending paged inference engine that streams a model's experts from disk instead of loading the whole thing into RAM at once. That's how a 397-billion-parameter model runs at roughly 14 GB peak memory on a 64 GB Mac, weights that total around 209 GB on disk. If the mechanics interest you, see what paged MoE inference is.

What's the honest trade-off?

Cloud still wins the hardest frontier reasoning, and it's faster. Outlier's Core 27B runs about 20.7 tokens per second on an M1 Ultra, slower than cloud flagships. For a single tricky math proof or the absolute bleeding edge, the giant rented models have the lead. The case for local isn't that it beats the cloud on raw IQ. It's that it's private, has no caps, works offline, and can't be taken away: the subscription you stop needing for most daily work, not a benchmark trophy.

What tools run local AI?

A few good ones, and they're not all the same kind of thing. Ollama is a free, open-source command-line runner that's a developer favorite. LM Studio and Jan are free desktop apps with a chat window for people who don't live in a terminal. All three run open-weight models and are bound by one rule: the model must fit in your RAM. Outlier is the Mac-native, batteries-included option: one signed download, no terminal or Docker, with chat in the box, and a coding agent, deep research, and vision on Pro, plus the paged engine that runs models bigger than RAM. Its Nano, Lite, Quick and Core models are published on HuggingFace (Plus and Vision 3.8 come from public community conversions of Qwen's models), and you can import your own MLX model too.

Questions about local AI