Yes — MLX & llama.cpp makes it far more capable than most people expect. All three Apple platforms can run AI entirely on-device, with no internet connection required once the model is downloaded. The experience is best on Apple Silicon Macs, very good on M-series iPads, and increasingly practical on modern iPhones.
This guide covers exactly what is possible on each device, which models run well, and what you need to get started.
Why Running AI Offline on Apple Hardware Is Now Practical
For most of AI's history, running a capable language model required a data center — or at least a workstation with a dedicated GPU. Two things changed that for Apple users:
- Apple Silicon's unified memory architecture — The CPU, GPU, and Neural Engine share the same high-bandwidth memory pool. A 7B parameter model that would have required a dedicated GPU now fits comfortably into the shared memory.
- Quantized open-source models — Projects like llama.cpp and MLX framework compress state-of-the-art models into files small enough to run on consumer hardware without sacrificing usable quality.
The result: a Mac, iPad, or iPhone can now run AI that would have required cloud infrastructure two years ago — entirely offline, entirely on your device.
Running AI on Mac Without Internet
Mac is the strongest platform for local AI. Apple Silicon Macs — can run models up to 14B parameters at practical speeds depending on the amount of RAM it has. An M3 MacBook Pro with 24 GB of unified memory handles a 7B model at 40–60 tokens per second, which is faster than most people read.
The two frameworks that make it work
MLX is Apple's own machine learning framework, built specifically for Apple Silicon. It distributes inference across the CPU, GPU, and Neural Engine simultaneously. MLX models are available directly from Hugging Face and offer the fastest local inference.
llama.cpp is an open-source inference engine that runs on any Mac — including older Intel models — without requiring Apple Silicon. It is slightly slower than MLX on M-series chips but fully cross-platform and widely supported.
Models that run well on Mac
| Model | Size on disk | Min RAM | Best for |
|---|---|---|---|
| Qwen 2.5 3B | ~2 GB | 16 GB | Fast Q&A, summaries |
| Llama 3.2 8B | ~5 GB | 24 GB | General reasoning |
| Gemma 3 4B | ~3 GB | 16 GB | Instruction following |
| Qwen 2.5 14B | ~9 GB | 24 GB | Complex reasoning, long docs |
| Qwen 2.5 32B | ~20 GB | 48 GB | Near-GPT-4 quality, locally |
Once the model is downloaded, your Mac runs it with no network connection. Wi-Fi off, airplane mode, remote cabin with no signal — the model works identically in all of these conditions.
Running AI on iPad Without Internet
iPads with M-series chips (M1, M2, M4) have the same Neural Engine as Mac and handle 3B–7B models comfortably. An M2 iPad Pro with 8 GB of RAM runs a 4B model at a usable speed for chat and document Q&A.
Older A-series iPads are more limited. The A14 Bionic and later can run 1B–3B models via llama.cpp, but responses are noticeably slower. If you are buying an iPad specifically for local AI, an M-series chip makes a material difference.
What works well on iPad
- Summarising documents and PDFs
- Answering questions about notes or research
- Drafting text from prompts
- Running a private knowledge base from imported files
What does not work well on iPad is very large models (14B+) — they are too slow for interactive use. For iPad, 4B–7B is the practical sweet spot.
Running AI on iPhone Without Internet
iPhones with A16 Bionic or later (iPhone 14 Pro and newer) can run 1B–3B models locally. The A17 Pro and A18 chips have significantly faster Neural Engines and handle these smaller models at a speed that feels responsive.
What 1B–3B models can do well on iPhone:
- Short-form Q&A
- Summarising short documents or notes
- Text generation for drafts and messages
- Classification and extraction tasks
7B models are technically possible on high-end iPhones but are slow enough to be impractical for interactive use. The iPhone use case today is smaller, faster models — and those are genuinely useful for many real-world tasks.
What You Need to Get Started (One-Time Setup)
Running AI offline on any Apple device follows the same pattern:
- Install an on-device AI app — Kynora provides the runtime (MLX or llama.cpp) and the interface.
- Download a model — model files range from ~1 GB (small) to ~20 GB (large). This download happens once, over your internet connection.
- Use it offline — after that, disconnect from the internet entirely. The app and model function identically.
The model files live in your app's local storage, not in the cloud. There is no login, no subscription check, and no server call during inference.
The Document Use Case: Private AI on Your Files
General-purpose local AI chat is useful. But the most compelling offline AI use case for professionals is applying that AI to your own documents — contracts, research papers, case files, reports — without those files ever leaving your device.
This is what RAG (Retrieval-Augmented Generation) does: your documents are indexed locally, and when you ask a question, the AI searches your private knowledge base and generates an answer grounded in your actual files, with citations back to the source.
This is the core of Kynora — a private knowledge workspace for Mac, iPad, and iPhone that runs the entire pipeline locally: document parsing, embedding, vector search, and LLM inference. No cloud, no accounts, no data leaving your device. Join the waitlist to be first to know when it launches.
Frequently Asked Questions
Do I need an Apple Silicon Mac, or does this work on Intel Macs too?
It works on Intel Macs via llama.cpp, but performance is noticeably slower. A 7B model that can run at 40 tokens/second on an M2 MacBook might run at 8–12 tokens/second on an Intel Mac. Smaller models (1.5B) are more practical on Intel. Apple Silicon is strongly recommended for a comfortable experience.
How much storage do I need?
A small model like Qwen 2.5 3B takes about 2 GB. A mid-size 7B model takes about 5 GB. Large models like Qwen 14B take about 9 GB. If you plan to use multiple models, a Mac with 512 GB of storage or more is comfortable.
Is the quality as good as Cloud AI tools?
For 3B–7B models running locally: not quite. For 14B+ models on a well-specced Mac: close enough for most professional document tasks. The trade-off is that local models are private and offline; cloud models are larger and more capable at complex reasoning. Many professionals find the privacy guarantee worth the quality difference for sensitive work.
What happens if I lose internet mid-session?
Nothing. On-device AI has no dependency on network connectivity during inference. If you are mid-conversation and your Wi-Fi drops, the model keeps running. This is one of the practical advantages of local AI over cloud tools.
Can I use it on a plane?
Yes. Once the model is downloaded, airplane mode does not affect it. People who travel frequently find this particularly useful — sensitive documents and a capable AI, both available without needing to connect to anything.