Yes — Apple Silicon MacBooks are genuinely good at local AI because the GPU shares the machine's unified memory, so your total RAM is effectively your VRAM budget; what size you can run depends on how much RAM you have.
Apple Silicon MacBooks (M1, M2, M3, M4 and their Pro/Max/Ultra variants) are one of the better places to run open models locally, and the reason is an architectural quirk that works in your favor: unified memory. On a PC, VRAM is a fixed, separate pool soldered to the graphics card, and it's usually the hard limit on what you can run. On Apple Silicon there's no separate VRAM number — the CPU and GPU share one pool of memory, so your total system RAM (minus what macOS needs) is effectively your VRAM budget. A 32 GB or 64 GB Mac can therefore hold models that would need an expensive dedicated GPU on a PC.
What you can actually run comes down to how much RAM your Mac has, using the standard sizing rule: budget about 0.5 GB per billion parameters at the common 4-bit quant, plus a few gigabytes of headroom for the KV cache and for macOS itself. On an 8 GB MacBook, stick to small models — 3B and 7-8B class at 4-bit run fine, though headroom is tight. On 16 GB, you're comfortable with 7-8B models and can reach into the low-teens-billion range. On 32 GB, mid-size models around 14B–32B become very usable. On 64 GB or more, you can run 70B-class models and large Mixture-of-Experts models that would need multiple GPUs on a PC. This is why high-RAM Macs became popular with local-AI enthusiasts.
The easiest way to start is a friendly runner. Ollama (command-line) and LM Studio (a graphical app) both run natively on Apple Silicon, download a ready-to-go quantized GGUF for you, and use the Mac's GPU automatically via Apple's Metal framework — no configuration. You install the app, pick a model that fits your RAM, and you're chatting in minutes. Under the hood they use llama.cpp, which has strong Metal support, so performance on Apple Silicon is genuinely good, not an afterthought.
A few honest caveats. First, Macs are excellent for running models but not the platform of choice for training or fine-tuning large ones — that ecosystem is still built mostly around NVIDIA GPUs. Second, generation speed on a Mac is solid for chat and coding but a high-end NVIDIA card will generally produce tokens faster; for a single user, though, a Mac is usually more than fast enough to be pleasant. Third, an Intel (non-Apple-Silicon) MacBook lacks the unified-memory advantage and will be much slower, effectively CPU-only — the whole story above is about Apple Silicon.
Mixture-of-Experts models are a particularly nice fit for big Macs. Because an MoE model only activates a few of its experts per token, it runs at a usable speed even when it's large in total, and the unified-memory pool can hold the whole thing. That's why a 64 GB+ Mac is a favorite for running large MoE flagships that would otherwise require a multi-GPU rig.
The cost story is the appealing part: once you own the Mac, running models on it costs only electricity, everything stays offline and private, and there's no per-token bill no matter how much you use it. The limit is simply your RAM — bigger models won't fit or will spill and slow down. If a model you want is too big for your Mac, the honest alternatives are a smaller model that fits, renting a GPU by the hour, or a pay-per-token API with your own key, and which is cheapest depends on your usage.
Spanvero treats a Mac's unified memory exactly like a VRAM budget when it computes what fits, so you can filter to models sized for your machine. Browse what runs in a 16 GB budget at /models/16gb-vram/ or a 24 GB budget at /models/24gb-vram/, see the laptop-friendly picks at /best/best-llm-for-a-laptop/, and use /calculator/ to enter your Mac's RAM and a specific model to see whether it fits and what it would cost versus renting or an API.
How much RAM vs VRAM do I need for LLMs? · VRAM · Do I need a GPU to run local AI? · Ollama · LM Studio · What LLMs can I run on 16GB of VRAM? · Quantization · Mixture of Experts (MoE)
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