Local AI hardware

Best Mac for Local LLMs: Unified-Memory Tiers and Model Fit

For local LLM inference on Apple Silicon, unified-memory capacity is often the first buying decision. A faster chip cannot compensate for a model that does not fit comfortably in available memory.

Can a Mac mini run an LLM locally?

Yes. Apple Silicon Macs can run local language models using runtimes designed for Metal and unified memory. The practical limit is usually the amount of unified memory available after allowing room for macOS, the inference runtime, context, and other applications.

Can I run an LLM locally on my Mac?

Yes. A Mac can run the model entirely on your own machine, which can be useful for privacy, offline development, experimentation, and avoiding per-token API charges. Larger models require more unified memory and may run considerably slower than smaller quantized models.

How much RAM do I need for a local LLM?

Start with the model's weight footprint and add headroom for runtime overhead and context. Roughly speaking, a 4-bit model needs about 0.5GB of weight memory per billion parameters before overhead, but the real requirement should be measured with the exact model and runtime.

What is the cheapest way to run LLMs locally?

The lowest-cost approach is usually to use hardware you already own and select a model and quantization that fit comfortably. Buying additional hardware becomes more attractive when local inference is frequent enough that privacy, availability, or avoided cloud usage justifies the purchase.

Buy memory for the model you actually want to run

A rough 4-bit weight estimate is about 0.5GB per billion parameters before runtime overhead. That means a 27B model starts around 13.5GB for weights alone, while a 70B-class model starts around 35GB before context, framework overhead, and other applications.

Why unified memory is useful for local AI

Apple Silicon uses one unified memory pool accessible by CPU and GPU. This allows machines with large unified-memory configurations to run models that would exceed the VRAM capacity of many consumer discrete GPUs, although speed and software support still depend on the runtime and workload.

Do not choose solely from synthetic benchmarks

Tokens per second matters, but model fit, context size, prompt-processing speed, concurrency, power use, reliability, and whether the machine will perform other work at the same time can matter more for an always-on agent node.