The Best Mac for Local LLMs Is the One With Enough Memory

When it comes to running local large language models (LLMs) on a Mac, unified memory matters more than the number of graphics processing unit (GPU) cores. On the sites and systems we build and operate, a machine stops being useful once it runs out of memory, no matter how fast the chip is.

Apple introduced the M6 and M5 Ultra on August 25, 2026. The M6 is available in the Mac mini with up to 32GB of unified memory and up to 170GB/s of memory bandwidth. The M5 Ultra comes in the Mac Studio with up to 512GB and 1.2TB/s (Apple).

Those numbers make most of the buying decision for you. The rest of the spec sheet affects how quickly a model responds. Memory determines whether the model can load in the first place.

Why Unified Memory Sets the Ceiling

A model has to fit in memory before it can run at a usable speed. On Apple silicon the CPU and GPU share one pool of unified memory. The size of that pool is the size of the largest model you can hold.

The arithmetic is simple enough to do before you buy. Multiply the parameter count by the bytes per parameter that your quantization uses, then leave headroom for context and the operating system.

At 4-bit quantization each billion parameters costs roughly half a gigabyte. A 70-billion-parameter model lands near 35GB before context, which is why a 32GB machine cannot hold it and a 64GB machine can.

• 4-bit quantization: about 0.5GB per billion parameters. An 8B model needs roughly 4GB, a 70B model roughly 35GB.
• 8-bit quantization: about 1GB per billion parameters, with better output quality and double the memory cost.
• Context window: long prompts and long conversations add memory on top of the weights, so budget headroom rather than buying exactly enough.
• The operating system: macOS needs its own share, so treat usable memory as meaningfully less than the number on the box.

What the M6 and M5 Ultra Actually Hold

The M6 ships in the Mac mini on a 2-nanometer process. It pairs a 12-core CPU with a 12-core GPU and a dual 16-core Neural Engine, with up to 32GB of unified memory and up to 170GB/s of bandwidth (Apple).

The M5 Ultra ships in the Mac Studio. It runs up to a 36-core CPU and up to an 80-core GPU with a 32-core Neural Engine, up to 512GB of unified memory, and 1.2TB/s of bandwidth (Apple).

Apple states the M5 Ultra can run huge LLMs with hundreds of billions of parameters entirely on device. It says the M6 can run LLMs on device for secure and private agentic tasks (Apple). Confirm current configurations and prices on Apple's own Mac lineup page before ordering.

• M6, Mac mini: up to 32GB unified memory, up to 170GB/s, 12-core CPU, 12-core GPU, dual 16-core Neural Engine, 2nm.
• M5 Ultra, Mac Studio: up to 512GB unified memory, 1.2TB/s, up to 36-core CPU, up to 80-core GPU, 32-core Neural Engine.
• Apple's stated AI gains: up to 2x peak Neural Engine compute on M6, and up to 4.5x peak GPU compute for AI on M5 Ultra against M3 Ultra.
• Bandwidth matters for speed: 1.2TB/s against up to 170GB/s is roughly a sevenfold gap, and token generation speed tracks bandwidth closely.

What Runs on Which Mac

A 32GB Mac mini comfortably runs models up to about 13 billion parameters at 4-bit, with room for a real context window. That covers most drafting, summarizing, and classification work a small team wants kept in-house.

A 64GB to 128GB machine opens the 70-billion-parameter class, which is where local output starts to feel close to a hosted frontier model on ordinary business tasks.

512GB is a different category of purchase. It exists so a machine can hold models with hundreds of billions of parameters without touching a datacenter, and few teams need that on a desk.

• Up to 32GB: 7B to 13B models at 4-bit. Document summarizing, classification, drafting, and retrieval over your own files.
• 64GB to 128GB: the 70B class at 4-bit, with headroom for long context. The usual sweet spot for a team that wants private inference.
• 192GB and up: mixture-of-experts models and very large open-weights releases, plus running more than one model at once.
• 512GB: hundreds of billions of parameters on device, per Apple's own claim. A research or regulated-data purchase rather than a productivity one.

When a Mac Beats a GPU Box, and When It Loses

A Mac wins on memory per dollar and on power draw. Getting 128GB or more attached to a GPU means several cards, a chassis to hold them, and a power budget most offices do not have.

A machine with NVIDIA cards wins on raw throughput and on software support. Most inference and fine-tuning tooling targets CUDA first, and some of it never gets a Metal path at all.

The split is cleaner than it sounds. Buy a Mac to run a large model privately at conversational speed for a handful of people. Buy GPUs to serve many users at once, or to fine-tune anything.

• Choose a Mac: single-user or small-team private inference, large models, quiet office, no rack, no CUDA requirement.
• Choose GPUs: concurrent users, production serving, fine-tuning, or a tool that only ships CUDA support.
• Choose neither: bursty or occasional workloads, where a hosted open-weights API costs less than any hardware you would leave idle.

Who Should Not Buy a Mac for This

Do not buy one to serve a production application. Apple silicon handles one conversation at a time well and degrades quickly under concurrent requests, so a customer-facing service belongs on GPUs or a hosted API.

Do not buy one to fine-tune models. The tooling assumes CUDA, and the workarounds cost more engineering time than the hardware saved.

If the reason you want local inference is privacy rather than cost, price a hosted open-weights API with a no-training-on-your-data term first. It often ends the requirement without any purchase, and our guide to running open-weights models walks through the deployment paths.

What Would Change This Answer

Wider Metal support in the fine-tuning stack would move the recommendation. The CUDA dependency is the main reason a Mac loses jobs it has the memory to do.

A cheaper path to high-memory GPU configurations would do the same from the other direction. The Mac advantage today is memory per dollar, and that advantage is a pricing fact rather than a permanent one.

Model efficiency is the third variable. Every release that delivers 70-billion-parameter quality at 13 billion parameters moves work down a tier of hardware, which is the cheapest upgrade anyone gets.

The practical next step is to name the largest model you intend to run, do the memory arithmetic above, and buy the Mac one tier above the answer.