Quick Answer: The DGX Spark and the Mac Studio both give you a quiet, desk-sized box with roughly 128 GB of unified memory for local LLMs — but they’re built for different buyers. The DGX Spark’s edge is native CUDA and a Grace Blackwell (GB10) stack that mirrors NVIDIA’s data-center tooling, plus the ability to link two units for models up to 405B parameters. The Mac Studio’s edge is memory bandwidth — the M3 Ultra’s ~819 GB/s is roughly 3× the Spark’s 273 GB/s, so it generates tokens faster on any model that fits both. Buy the Spark for CUDA; buy the Studio for speed per dollar and a machine that’s also a great workstation.
The two machines at a glance
| NVIDIA DGX Spark | Mac Studio (M3 Ultra) | |
|---|---|---|
| Chip | GB10 Grace Blackwell | Apple M3 Ultra |
| Unified memory | 128 GB LPDDR5x (64 GB variant too) | Configurable, up to Apple's top tier |
| Memory bandwidth | ~273 GB/s | ~819 GB/s |
| Ecosystem | Native CUDA | macOS / Metal (MLX, llama.cpp) |
| Scale-out | Link 2 units → up to 405B params | Single box |
| Doubles as | Dedicated AI dev box | Full macOS workstation |
Memory bandwidth is the headline
Both boxes can hold big models — NVIDIA cites up to 200B parameters on a single DGX Spark, and the Mac Studio’s top configs reach similar territory. The difference buyers underestimate is bandwidth. Local-LLM generation is memory-bound: every token read pulls the model weights through memory. The Mac Studio M3 Ultra’s ~819 GB/s is roughly three times the DGX Spark’s 273 GB/s, so on a model that fits both, the Mac generates noticeably faster.
That’s the honest framing for this whole class of hardware: a discrete GPU with far less memory but much higher bandwidth will out-run both of these on any model small enough to fit its VRAM. You buy a 128 GB box to run models that won’t fit a GPU — and between these two, the Mac is the faster of the pair.
When the DGX Spark wins
- You need native CUDA. If your stack is built on CUDA-only libraries, the Spark runs them unchanged — no ROCm or Metal translation layer.
- You’re prototyping for NVIDIA data-center deployment. The Spark mirrors the software stack you’ll ship to, making it a genuine mini dev box for Grace Blackwell targets.
- You want to scale to very large models. Two linked DGX Sparks reach up to 405B parameters — beyond what a single Mac Studio holds.
NVIDIA DGX Spark (GB10, 128 GB)
- 128 GB unified memory, GB10 Grace Blackwell, 4 TB SSD — native CUDA out of the box.
- Link two for models up to 405B parameters; a true desktop NVIDIA AI stack.
- Lower bandwidth (273 GB/s) means it holds big models better than it races them.
A quick availability note: the DGX Spark sells mainly through NVIDIA’s marketplace and partners such as Micro Center, so stock on general retail is thin — check the listing before counting on it. Buying one for a lab or team? A free Amazon Business account adds VAT invoices and quantity pricing on the gear you can source there.
When the Mac Studio wins
- You want faster generation per dollar. The M3 Ultra’s bandwidth advantage makes it the quicker of the two on most models.
- You want one machine for everything. The Studio is a full macOS workstation as well as an LLM box — video, code, design, and AI on the same desk.
- You value a mature local-LLM toolchain on Apple Silicon. MLX and llama.cpp are well supported and easy to set up.
Apple Mac Studio (M3 Ultra)
- ~819 GB/s memory bandwidth — the fastest token generation in this comparison.
- Configurable unified memory for 70B and 120B-class models.
- Doubles as a top-tier creative/development workstation.
See our full best Mac for local LLMs guide for how the Mac mini and M4 Max Studio fit in below the Ultra.
Price: a moving target
The DGX Spark launched at $3,999 for the 128 GB model in late 2025, then NVIDIA raised the MSRP (to around $4,699 in early 2026, citing memory-supply constraints), and later 128 GB listings reached about $6,950. A cheaper 64 GB variant launched through partners from about $4,999. Mac Studio pricing depends heavily on the memory tier you configure. Because both move with memory-chip supply, treat every number here as a class, not a quote — check live listings before you buy.
The cheaper alternative to both
If native CUDA isn’t a must and you can live with the lowest bandwidth of the three, a 128 GB Strix Halo mini PC (Framework Desktop, GMKtec EVO-X2, Beelink GTR9) gets you into the same 128 GB unified-memory territory for less than either box here — the budget entry point to large-model capacity.
The bottom line
Buy the DGX Spark if you need native CUDA, a data-center-mirroring dev box, or dual-unit scaling to 405B parameters. Buy the Mac Studio (M3 Ultra) if you want faster generation per dollar and a machine that’s also a complete workstation. Both are capacity plays — for pure speed on models under ~32B, a discrete GPU still wins, and a Strix Halo mini PC is the cheaper route to 128 GB if CUDA isn’t essential.