The short answer
A 16GB Apple Silicon Mac can be a useful starting point for smaller quantized models. 24GB gives more room for a mid-size model and everyday apps; 32GB increases context or precision options. Larger capacities can accommodate larger workloads, but physical memory is not all available to the model. Choose by an explicit memory budget and a real task, not a parameter-count slogan.
Unified memory is shared with the rest of your Mac
Apple Silicon allows CPU and GPU work to access shared memory. This can make large memory configurations useful for local inference, but a 24GB Mac does not provide 24GB of dedicated model memory. macOS, your browser, editor and other applications use that same capacity.
Leave an explicit reserve instead of trying to consume every available gigabyte. 99Tokens uses a 25% default reserve for the Mac examples below. That is a planning assumption, not an Apple guarantee or a universal Metal allocation limit. Change it to match your machine and leave additional headroom for unexpected peaks.
16GB vs 24GB vs 32GB vs 64GB: a planning table
These examples use documented Mac mini M4 and M4 Pro configurations to hold the capacity labels to real hardware. They are not a claim that these are the only Mac options or the best current purchase. Check the exact chip and configuration you are considering.
| Mac configuration | Budget | What to investigate |
|---|---|---|
| 16GB Mac mini M4 | 12 GiB | Smaller quantized models; test a mid-size model with your everyday apps open. |
| 24GB Mac mini M4 | 18 GiB | More room for a 14B Q4 model and a longer conversation. |
| 32GB Mac mini M4 | 24 GiB | Compare more context with higher precision instead of spending both budgets at once. |
| 64GB Mac mini M4 Pro | 48 GiB | Evaluate larger artifacts or multiple workloads, with separate speed and compatibility checks. |
A 14B example shows why context changes the answer
For Ministral 3 14B, the planner estimates about 7.8 GiB of Q4 weights, using 13.945 billion stored parameters and 4.8 effective bits per weight. At 8,192 tokens, FP16 cache adds 1.25 GiB. With a 1 GiB runtime allowance, the total is approximately 10.0 GiB before the separate system reserve.
At 32,768 tokens, that cache becomes 5 GiB and the total becomes approximately 13.8 GiB. The same model now exceeds the 12 GiB planning budget on a 16GB Mac, while it remains below the 18 GiB budget on the 24GB example. This is arithmetic from the model architecture, not a measured result on either Mac.
The calculation covers one text request. Exact quantized tensors, the runtime’s cache layout and non-text inputs can change the allocation. A memory estimate that fits is a reason to test the workload, not proof that the runtime supports it or that its speed will suit you.
More memory and a faster chip solve different problems
Capacity answers whether the workload can fit. Bandwidth and compute are separate constraints on performance. Apple lists 120 GB/s memory bandwidth for the M4 Mac mini and 273 GB/s for the M4 Pro configuration. Those specifications are not tokens-per-second measurements and do not establish a fixed speed multiplier.
If your workload already fits, buying more capacity alone does not tell you how much faster it will run. If it does not fit, a faster chip with the same capacity does not remove that memory shortfall. Identify the limiting condition before choosing the upgrade.
For an existing Mac, compare two smaller models on work you actually do. For a planned purchase, keep a record of the artifact, context and application memory you want to support, then compare exact configurations against that requirement.
Ollama, LM Studio and MLX: check the runtime too
LM Studio documents Apple Silicon support and recommends at least 16GB RAM; its requirements page also notes that smaller models and modest contexts may work on 8GB Macs. Check the current macOS requirement before installing.
LM Studio supports an MLX runtime as well as its GGUF workflow. Ollama also documents Apple GPU support. These are runtime choices to evaluate with a supported artifact; a shared model name does not make differently converted files equivalent.
Choose one application and one supported artifact for the first test. Add another runtime only when there is a specific memory, speed or feature question to answer. This makes it easier to tell whether a change helped.
Validate with Activity Monitor and your longest task
Open Activity Monitor’s Memory tab before loading the model. Apple explains memory pressure and swap as indicators of how the machine is using its memory resources. Watch them during the task, not just while the model is idle.
Repeat the task with your normal apps open, then try the longest prompt or chat you expect. Record responsiveness, the time until generation starts, and whether swap keeps growing. A model that opens successfully can still be an impractical daily workload.
If memory pressure rises, first reduce context, close an unused loaded model or choose a smaller artifact. If memory remains comfortable but responses are slow, investigate the runtime and generation phases. Keep the distinction between a memory problem and a speed problem clear.
See it on your own setup.
Change the model, hardware or context and see the memory budget update.
Try this example