Mac Studio vs RTX Workstations for Local LLMs: Unified Memory vs VRAM
Two different answers to the same question: how do I get a lot of fast memory for local models? Apple's answer is unified memory, one pool shared by CPU and GPU, scaling to 512 GB in the Mac Studio line in this directory. NVIDIA's workstation answer is VRAM on a discrete card, topping out at 96 GB on the RTX PRO 6000 Blackwell, inside a tower you assemble or buy whole.
The trade is ecosystem against capacity. CUDA is where the tooling, the examples and the fixes live, and a 96 GB card runs the mainstream stack without translation. Unified memory wins on sheer room: a 70B model at FP16 wants about 168 GB with overhead, which no single workstation card holds, but a high memory Mac Studio does, at lower speed than a GPU would manage. Speed versus fit, again: the same fork as everywhere in local AI.
The memory leaders, both camps
| Machine or card | Memory type | Memory | US list price |
|---|---|---|---|
| Apple Mac Studio M3 Ultra 512GB 4TB | Unified memory | 512 GB | $11,499 |
| Apple Mac Studio M3 Ultra 256GB 4TB | Unified memory | 256 GB | $8,099 |
| Apple Mac Pro M2 Ultra 192GB 8TB | Unified memory | 192 GB | $10,799 |
| Apple Mac Studio M4 Max 128GB 2TB | Unified memory | 128 GB | $4,099 |
| Apple Mac Studio M3 Ultra 96GB 1TB | Unified memory | 96 GB | $3,999 |
| NVIDIA B300 SXM | VRAM (add a workstation) | 288 GB | $69,999 |
| NVIDIA GB300 NVL72 | VRAM (add a workstation) | 288 GB | $3,750,000 |
| AMD Instinct MI350X | VRAM (add a workstation) | 288 GB | $39,999 |
| AMD Instinct MI355X | VRAM (add a workstation) | 288 GB | $42,999 |
Memory on offer
Price honesty note: several Mac configurations and all the big workstation cards lack list prices in our data, the cards because they are quote sold, some Mac configs because we verified the base price only. The table shows what is actually published.
Thermals and noise are the unlisted specs in this fight. The Mac Studio does its work at desk friendly volume under sustained load, while a tower with a 600 W workstation card needs case airflow to match, a real cost in a small studio or a shared office. Nobody publishes a noise column, so the directory cannot chart it, but buyers hear the difference every day and should weigh it alongside the memory math.
Our read: if your stack is MLX, llama.cpp or ComfyUI friendly and the models are big, unified memory is the calm choice. If you need CUDA, training frameworks or the widest software net, the RTX workstation is home. Both camps are filterable on the workstation page and the GPU page.