Local AI on a Mac: run LLMs and agents on your own machine.

What "local AI on a Mac" actually means

Running AI locally means the model itself — the multi-gigabyte file of weights that does the thinking — lives on your Mac's disk and runs on your Mac's chip. When you type a prompt, nothing is sent to a data center. The text is processed on your own GPU, the answer is generated on your own machine, and when you close the lid the whole system goes with you. No account, no per-token bill, no usage log on someone else's server.

Macs are unusually good at this, and the reason is Apple Silicon's unified memory. On a typical PC, a model must fit inside the graphics card's dedicated VRAM — often 8GB or 12GB — or spill into slow system RAM. On an M-series Mac, the CPU and GPU share one pool of fast memory, so the GPU can address almost all the RAM in the machine. A 32GB MacBook effectively has more usable "VRAM" than most consumer graphics cards, and the Metal acceleration built into runtimes like Ollama and LM Studio uses it automatically. That is why a quiet, fanless laptop can hold a fluent conversation with a 12B-parameter model while you keep working in other apps.

The practical payoffs are the ones covered across this hub: privacy (client records, contracts and code never leave the machine), cost (a model you have downloaded is free to run forever, no matter how many tokens you burn), and control (the model does not change or disappear because a vendor deprecated it). The honest trade-off is capability — the best local models trail the frontier cloud models — which is why most of the guides below recommend a hybrid setup: local by default, with an API key held in reserve for the hardest tasks.

Hardware: how much Mac do you need?

Any Apple Silicon Mac — M1 or later, including the base MacBook Air — can run local models. What changes with the hardware is which models, and how comfortably:

• 8GB: workable for small models (3B–4B class) but tight once a browser is open. Fine for experimenting, frustrating as a daily setup.
• 16GB — the practical minimum: runs 7B–8B quantized models smoothly alongside your normal apps. This is enough for real summarization, drafting and light agent work.
• 32GB — the sweet spot: runs 14B–30B-class models, which is where local models start reliably following instructions and calling tools. If you are buying a Mac partly for local AI, this is the configuration to aim for.
• 64GB and up: opens 70B-class models and long-context work. Genuinely nice, rarely necessary for desktop workflows.

Chip generation matters less than memory: an M1 Pro with 32GB beats an M4 with 16GB for this workload. For the deeper version — quantization levels, context-window memory costs, and a per-tier model shortlist — read the GGUF model guide and the 2026 model roundup.

The stack: a runtime, then the apps that use it

A working local-AI setup on a Mac has two layers. The first is a runtime — the engine that loads a model and serves it to other software through a local API. The two that matter are Ollama, a lightweight server you drive from the command line and script against, and LM Studio, a GUI with built-in model discovery and chat. They are not rivals so much as different doors into the same models; the comparison below walks through when each fits, and many people simply install both.

The second layer is the software that puts the model to work. A runtime on its own just answers chat messages; the point of local AI is connecting it to your actual files, browser and desktop. That is what the three apps on this site do:

• MultiAgentOS — a native desktop workspace where a local (or API) model works through supervised sidecars: browser, desktop, code, terminal, file, screenshot and subagents. Point it at Ollama, LM Studio or a GGUF file directly, and it becomes a full agent that drafts documents, fixes builds and clears research backlogs on your machine.
• LLM Browser — a Chrome-like browser built for humans and AI agents together. An LLM can navigate, click, fill forms and report back through a visible bridge panel and a local JSON-RPC/WebSocket API, so web-heavy chores run themselves while you watch.
• Codemonkey AI — a fully local coding tutor: 28 learning tracks, runnable lesson steps and guided projects, with the AI tutor answering from a local model so your early fumbling code never leaves the machine.

Everything below is organized around that stack: get a runtime working, connect an app, then go deeper on models, tools and costs.