Run Local LLM on your Mac
Choose from Qwen, Gemma, Llama and 1,000+ more models and run them on your Mac with Apple Silicon optimization.
What is Apple Silicon?
Apple's own line of chips, M1 and up, in every Mac since 2020. Three parts of that design decide which models your Mac can run and how fast.
One chip
The CPU, the GPU, the memory and the Neural Engine all sit on a single piece of silicon. Before the M1, a Mac had an Intel processor and, sometimes, a separate graphics card bolted alongside it.
Unified memory
The CPU and GPU share one pool of memory instead of each keeping its own. Nothing has to be copied from one to the other, and the GPU can reach all of it.
Memory bandwidth
Generating text is limited by how fast weights can be read out of memory, not by raw compute. Apple Silicon moves a lot of data per second, which is why a laptop keeps up at all.
What you need to run a local LLM on Mac
Any M-series Mac will run a local model, and the memory you have decides how big that model can be.
How to run an LLM on your Mac
Three steps and about ten minutes, most of which is the model downloading.
Download the .dmg
One universal installer covers every Apple Silicon Mac on macOS 13 or newer. Drag Atomic Chat to Applications and open it.
Pick a model
Browse over a thousand open models and take one that fits your memory. The download happens once, and after that the model opens straight from your disk.
Start chatting
The model runs on your own chip, so the answers keep coming with no connection, no account and no cap on how much you ask.
Atomic Chat vs Local LLM tools
Atomic Chat is the only one of these you can run on all five platforms.
Learn more in our blog
How to Run an LLM Locally
Pick a model for your hardware, understand quantization, and set it up in a few clicks.
Best Local LLM Apps in 2026
The 10 best local LLM apps compared on interface, platform reach, openness and tool support.
How to Run DeepSeek Locally
Run DeepSeek R1 offline — which distilled sizes fit your hardware, and the step-by-step setup.
FAQ
What people ask before running a local LLM on a Mac.
Yes. An M-series Air handles 7B and 8B models on 8GB, and a 16GB Air is comfortable with 14B. It's fanless, so a long generation will warm it up and slow down a bit, but it works.
At 4-bit, roughly 8GB for 8B models, 16GB for 14B, 32GB for 32B, and 64GB and up for 70B. Unified memory is shared with the rest of macOS, so leave headroom for whatever else you have open.
Not on raw speed. A dedicated NVIDIA card wins on tokens per second. Apple Silicon wins on capacity: unified memory means a 64GB Mac loads models that would need a very expensive GPU to fit at all.
No. Atomic Chat needs an Apple Silicon Mac running macOS 13 or newer, so M1 and up.
Yes. Drop in a contract, a bank statement or a spreadsheet and ask about it. Reading and analysis happen on your Mac, so the file is never uploaded or logged.
For code on a Mac, Qwen 3.5 and the DeepSeek distills are the two most people land on. Both explain functions, hunt bugs and refactor proprietary code without any of it leaving the machine, which is the whole point when an NDA rules out the cloud.
Yes. There's a local OpenAI-compatible endpoint on macOS. Point Kilo Code, Cline or any OpenAI SDK at it, with no API key and no per-token billing.
No. It installs from a .dmg like any other Mac app: drag it to Applications and open it. There is no command line step, no package manager and no account to create.
Completely. Nothing is sent to a server, no account is required, and nothing is logged in the cloud. Your chats sit on your own disk.
Small ones, yes. Atomic Chat is on the App Store and Google Play, so you can keep a compact model on your phone and chat in airplane mode.
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