Shared memory is a different budget
Apple silicon uses unified memory shared by the CPU, GPU, operating system and applications. A machine with 64 GiB of unified memory does not have 64 GiB dedicated exclusively to an AI model.
This planner reserves 25% of the selected Mac configuration for the operating system and other work. That is a conservative planning policy, not a guarantee about the operating system’s GPU allocation. Check actual limits and workload memory on the machine.
Discrete VRAM is more isolated
On a conventional GPU workstation, VRAM is separate from system RAM. A 24 GiB GPU plus 64 GiB of system RAM does not make an 88 GiB GPU. Some runtimes offload parts of a model to the CPU, but the performance depends on CPU memory bandwidth, transfer costs and the model split.
NVIDIA hardware has a broad CUDA software ecosystem. Apple silicon uses paths such as Metal and MLX. Verify your exact runtime, model architecture, quantization format and features instead of assuming equal support.
Compare the entire ownership experience
A workstation can offer replaceable GPUs, more storage choices and different cooling options. A compact Mac can offer a simpler physical setup and large unified-memory configurations, but memory is selected when purchasing the machine.
Capacity alone is not speed. High-memory hardware may load a model that is still too slow for the task. Start with your representative prompt and measure response latency, output speed, power, noise and any competing applications. No live price comparison is claimed here.
See it on your own setup.
Change the model, hardware or context and see the memory budget update.
Try this example