The 512GB Mac Studio is the machine local-AI people keep asking about. Here's what Apple actually ships, and what that much memory gets you.
The specs that matter
From Apple's announcement (Aug 25, 2026) and its tech specs page:
| M5 Max | M5 Ultra | |
|---|---|---|
| CPU | up to 18-core | up to 36-core |
| GPU | up to 40-core | up to 80-core |
| Unified memory | 36GB, up to 128GB | 96GB, up to 512GB |
| Memory bandwidth | up to 614GB/s | 1.2TB/s |
| Storage | up to 8TB | up to 16TB |
| Starting price (U.S.) | $2,499 | $5,499 |
The 512GB option needs the top M5 Ultra (36-core CPU, 80-core GPU). Apple said Mac Studio with 512GB was coming in late October, after general availability on Sept 22. It didn't list the 512GB price in the release, so check Apple's store before you budget.
Apple says M5 Ultra has up to 4.3x the peak AI compute of M3 Ultra, and its memory bandwidth is 50% higher than before. Those are Apple's numbers on Apple's tests.
Why 512GB is a big deal for AI
On a normal PC, the graphics card has its own memory (VRAM), and the model has to fit there to run fast. The biggest consumer card, the RTX 5090, has 32GB.
On Apple silicon, CPU and GPU share one pool. So the GPU can use most of that 512GB for model weights. That's how big open-weight models that would need several data-center GPUs can load on one desk machine. Apple pitches it as running "frontier-class" open models "entirely on device".
Rough rule of thumb from our VRAM guide: memory needed is about parameters × bytes per parameter, plus room for context. A 4-bit model takes about half a gigabyte per billion parameters. Use our local AI hardware sizing sheet to check a specific model.
The trade-off: bandwidth sets speed
Fitting a model and running it fast are different things. When generating text, each new token reads through the model weights, so memory bandwidth roughly caps tokens per second.
- M5 Ultra: 1.2TB/s.
- RTX 5090: 1,792GB/s, but only 32GB.
- Data-center GPUs with HBM go well beyond both.
So a huge model on a Mac Studio will run, but slower than the same model would on a rack of data-center GPUs. For a lot of solo builders, "runs at all, privately, on my desk" is the point.
Clustering
Apple says you can link several Mac Studios over Thunderbolt 5 with RDMA, pooling memory across machines. It claims a four-system cluster delivers up to 3x faster inference than one system.
Software
Apple points to MLX, its open-source ML framework, and a new Core AI framework. Popular local runners also support Apple silicon. If you're mixing local models with coding agents, see picking a coding agent.
Should you buy one?
That's your call, and it's a lot of money. Compare it against RTX Spark laptops with 128GB, a desktop GPU, or simply renting GPUs by the hour. We put all three side by side in unified memory explained.
