Running AI locally on a Mac is much more accessible than it was a few years ago. Apple Silicon in particular works well with small and medium language models because CPU, GPU, and a shared memory pool are closely integrated. The chip name alone still does not determine which model will run comfortably.
Unified Memory is the starting point
On Apple Silicon, the CPU and GPU share Unified Memory. A model accelerated by the GPU therefore does not necessarily need a separate VRAM pool in the way many Windows systems do. That simplifies planning, but macOS, other applications, model weights, and the active context all compete for the same memory.
8 GB: possible, but constrained
Small quantized models can run within 8 GB in some cases. There is little headroom for the operating system, browser tabs, documents, and longer context windows, though. It can work for experimentation but becomes restrictive for comfortable everyday local AI use.
16 GB: a more practical entry point
Sixteen gigabytes of Unified Memory provide a much more useful starting point for many small and medium quantized models. Models with several billion parameters can be practical when quantization and context length are chosen realistically.
24 or 32 GB: more room
Additional memory allows larger models, longer contexts, or more applications to stay open at the same time. It does not mean that twice the RAM automatically produces twice the model quality. Architecture, quantization, speed, and context requirements still matter.
The chip often changes speed more than fit
M1, M2, M3, M4, and their Pro and Max variants differ in memory bandwidth, GPU performance, and other characteristics. The same model may fit into memory on two Macs while responding at very different speeds.
Model size is not the same as file size
Labels such as 3B, 7B, and 14B roughly describe the number of model parameters. Quantization can make the model file far smaller than higher-precision storage. Runtime memory is also needed for context, caches, and other structures.
Context length also consumes memory
A model can load successfully and still run into limits with very long documents. A larger active context increases runtime memory use. The maximum advertised context length is therefore not automatically the best setting for your Mac.
Why Apple Silicon is practical
- shared memory for CPU and GPU
- energy-efficient local inference for many workloads
- no separate discrete-GPU setup required
- good fit for compact quantized models
- quiet operation for many everyday workflows
What to check before downloading a model
- How much Unified Memory does the Mac have?
- How large is the quantized model file?
- How much context do you actually need?
- How much memory do your other applications use?
- Is the runtime optimized for Apple Silicon?
Priviot and local AI
Priviot's local-AI path is designed around deliberately selected local models. The model should fit the device rather than simply being the largest option available. A bigger model is not useful if it constantly pushes the system to its memory limit.
See the Local AI section for more background.
Conclusion
For local AI on a Mac, Unified Memory is usually more informative than a model name by itself. Sixteen gigabytes provide much more flexibility than 8 GB for practical entry-level workflows, while 24 or 32 GB add useful headroom. The real fit depends on model size, quantization, context, and the speed you expect.