How much unified memory do I need for local AI?

24 GB comfortably runs 8B-14B models; 36-48 GB opens up 32B; 64 GB makes 70B practical; 128 GB+ reaches into very large models. Roughly two-thirds of unified memory is usable for the model.

Is a Mac faster than an Nvidia GPU for LLMs?

No. Per token, a fast Nvidia GPU is quicker. The Mac's advantage is capacity: it can fit much larger models in unified memory than a consumer Nvidia card, and it does so quietly and efficiently.

Which Mac chip is best for AI?

The Max and Ultra tiers, because they have the widest memory bandwidth and the most unified memory. A base M-chip works for small models but is bandwidth-limited for large ones.

Apple Silicon for Local AI: Unified Memory Explained (2026)

There's a quiet plot twist in local AI: one of the best machines for running big models isn't a tower with a roaring GPU — it's a MacBook. The reason is a single architectural choice called unified memory, and once you understand it, Mac pricing for AI suddenly makes sense.

What unified memory actually is

On a PC, your CPU has system RAM and your GPU has its own separate VRAM. A model has to fit in that VRAM — and consumer cards top out at 24–32 GB. Apple Silicon throws away that wall: the CPU and GPU share one pool of high-bandwidth memory. Buy a 64 GB Mac and the GPU can use most of that 64 GB for a model. No 24 GB ceiling.

The unlock A 64 GB MacBook Pro can run a 70B model at Q4 (~48 GB) that no single consumer Nvidia card can fit. That's the whole pitch — capacity that would otherwise cost you a multi-GPU server.

How much unified memory do you need?

Unified memory	Comfortably runs
16–24 GB	8B–14B models (MacBook Air / base Pro)
36–48 GB	Up to 32B-class models
64 GB	70B at Q4 becomes practical
128 GB+	Very large models, long context, headroom

Plan for roughly two-thirds of your unified memory being usable for the model — the OS and apps need the rest. So a 48 GB machine realistically gives a model ~32 GB.

The catch nobody mentions

Apple's advantage is capacity, not raw speed. Per token, a fast Nvidia GPU still wins — it has more memory bandwidth and compute. A Mac running a 70B model is genuinely usable but you'll feel it think. The honest framing: "slower and it fits" beats "fast and it won't load." If your priority is the biggest model on a portable, silent, efficient machine, Apple Silicon is brilliant. If you want maximum tokens-per-second and already own a model that fits 24 GB, Nvidia is faster.

The bandwidth also scales with chip tier — Max and Ultra chips have far wider memory buses than the base chips, which is why they feel much better on large models. If you're buying for AI, the memory amount and the chip tier matter more than the CPU core count.

Size a Mac for your model

Choose "Apple Silicon," pick a model and quant, and the calculator shows whether it fits in unified memory and an estimated speed — so you buy the right memory tier.

Open the Local AI Calculator →

FAQ

How much unified memory for a 70B model?

64 GB is the practical minimum, leaving the OS room while the model uses ~48 GB. 96–128 GB adds comfort and longer context.

Is a Mac mini good for local AI?

Yes for smaller models — configure it with as much unified memory as you can afford. The same memory rules apply as the MacBook line.

Does it run the same tools as PC?

Mostly. Ollama, LM Studio, and llama.cpp all run natively on Apple Silicon and use Metal for acceleration. Some Nvidia-only (CUDA) tools won't.

We may partner with companies or groups to affiliate hardware products based on user needs, earning a commission from qualifying purchases. Memory and speed figures are practical estimates and vary by chip tier, bandwidth, and runtime. Data current as of June 2026.