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DGX Spark vs Mac Studio M3 UltraSpecs, memory and LLM performance
Mac Studio M3 Ultra offers up to 512 GB of unified memory at 819 GB/s, 3× DGX Spark’s bandwidth. DGX Spark has 128 GB but more AI compute, NVIDIA’s CUDA software and a lower price.
Memory
128 GB vs 512 GB
Bandwidth
273 GB/s vs 819 GB/s
Power
240 W power supply vs Up to 480 W
Type
Desktop AI system vs Desktop computer
Which should you choose?
CHOOSE DGX SPARK IF
You build on CUDA, fine-tune models or need fast prompt processing at a lower price.
CHOOSE MAC STUDIO M3 ULTRA IF
You want the largest models on one desktop (up to about 600B parameters at 4-bit with 512 GB) and faster token generation, using MLX or llama.cpp.
Still deciding between DGX Spark and Mac Studio M3 Ultra?
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DGX Spark vs Mac Studio M3 Ultra specs
Manufacturer specifications. Green marks the stronger figure where a higher number is better. Dense compute is shown without sparsity.
| Specification | NVIDIA DGX Spark | Apple Mac Studio M3 Ultra |
|---|---|---|
| Type | Desktop AI system | Desktop computer |
| Memory | 128 GB | 512 GB (max) |
| Memory type | LPDDR5X unified memory | Unified memory (96 GB, 256 GB or 512 GB) |
| Memory bandwidth | 273 GB/s | 819 GB/s |
| FP16 / BF16 (dense) | Not published | Not published |
| FP8 (dense) | Not published | Not published |
| FP4 (dense) | 500 TFLOPS | Not published |
| Power | 240 W power supply | Up to 480 W |
| Multi-GPU link | ConnectX-7 200 Gb/s: link two units for 256 GB | Thunderbolt 5 and 10 GbE |
| Form factor | Palm-sized desktop with 4 TB NVMe | Compact desktop |
| Architecture | GB10 Grace Blackwell, 2025 | Apple M3 Ultra, 2025 |
| Software | CUDA, NVIDIA AI software, vLLM, llama.cpp, Ollama | MLX, llama.cpp, LM Studio, Ollama (no CUDA) |
| Price | US$3,999 at launch (4 TB) | From US$3,999 (96 GB); more for 256 GB and 512 GB |
What they mean for running LLMs
For large language models, memory decides which models fit and how many users you can serve, and memory bandwidth decides how fast each user gets tokens. These are planning figures for one GPU or system using a 4-bit model.
| For LLMs | DGX Spark | Mac Studio M3 Ultra |
|---|---|---|
| Usable for models | About 109 GB | About 384 GB (of 512 GB; macOS reserves part of unified memory) |
| Largest dense model at 4-bit (1 user, short context) | About 170B parameters | About 630B parameters |
| Llama 3.3 70B at 4-bit, single-user speed ceiling | About 7 tokens/s | About 21 tokens/s |
Which open-weight models fit
Memory needed for one user with 8K context at the lowest precision available for each model. Open a model for team-size and fine-tuning figures.
| Model (1 user, 8K context, lowest available precision) | DGX Spark | Mac Studio M3 Ultra |
|---|---|---|
| gpt-oss-20b 21B · about 15 GB at MXFP4 | Yes | Yes |
| Gemma 4 26B A4B 25.2B · about 16 GB at 4-bit | Yes | Yes |
| Qwen3.6-27B 27.78B · about 20 GB at 4-bit | Yes | Yes |
| Qwen3.8-27B 27.8B · about 24 GB at NVFP4 | Yes | Yes |
| Gemma 4 31B 30.7B · about 20 GB at 4-bit | Yes | Yes |
| Qwen3.6-35B-A3B 35B · about 24 GB at NVFP4 | Yes | Yes |
| Llama 3.3 70B Instruct 70.6B · about 46 GB at INT4 | Yes | Yes |
| gpt-oss-120b 117B · about 70 GB at MXFP4 | Yes | Yes |
| MiniMax-M2.5 229B · about 135 GB at 4-bit | 2 linked units | Yes |
| GLM-5.3-Flash 320B · about 192 GB at NVFP4 | 2 linked units | Yes |
| DeepSeek-V4.1-Flash 552B · about 320 GB at 4-bit | No | Yes |
| DeepSeek-R1 671B · about 410 GB at 4-bit | No | No |
| DeepSeek-V3.2 685B · about 419 GB at NVFP4 | No | No |
| GLM-5.2 744B · about 455 GB at NVFP4 | No | No |
| GLM-5.3 753B · about 460 GB at NVFP4 | No | No |
| GLM-5.1 754B · about 461 GB at NVFP4 | No | No |
| Kimi K2 1T · about 579 GB at 4-bit | No | No |
| DeepSeek-V4-Pro 1.6T · about 976 GB at FP4 + FP8 | No | No |
| Kimi K3 2.8T · about 1.62 TB at MXFP4 | No | No |
Models in amber need several of these GPUs working as one. Buying the GPUs is the easy part. Vanik Edge OS splits the model across them, keeps it running if a server fails and manages it all from one console. See how Edge OS clusters GPU servers
FROM SIZING TO RUNNING
Sizing the hardware is the easy part.
Getting several GPUs to work as one system for a whole company is where most projects slow down. That is the part Vanik Edge OS does for you.
STEP 01 · THIS TOOL
Size it
Work out the GPU memory your models need and which GPUs or servers can hold them.
STEP 02 · VANIK EDGE OS
Cluster it
Edge OS joins your GPU servers into one cluster, splits a large model across them and keeps it running if a server fails. NVIDIA and AMD servers work side by side.
STEP 03 · VANIK EDGE OS
Run it
Your team gets AI apps with single sign-on and an audit trail, managed from one console, with your data staying inside your building.
DGX Spark vs Mac Studio M3 Ultra questions
What is the difference between the DGX Spark and the Mac Studio M3 Ultra?
Mac Studio M3 Ultra offers up to 512 GB of unified memory at 819 GB/s, 3× DGX Spark’s bandwidth. DGX Spark has 128 GB but more AI compute, NVIDIA’s CUDA software and a lower price.
DGX Spark vs Mac Studio M3 Ultra: which is better for LLM inference?
For generating tokens, the Mac Studio M3 Ultra is faster: it has 819 GB/s of memory bandwidth versus 273 GB/s, and LLM token generation is mostly limited by bandwidth. The Mac Studio M3 Ultra holds larger models and more users’ context with 512 GB of memory versus 128 GB. Choose the DGX Spark if you build on CUDA, fine-tune models or need fast prompt processing at a lower price. Choose the Mac Studio M3 Ultra if you want the largest models on one desktop (up to about 600B parameters at 4-bit with 512 GB) and faster token generation, using MLX or llama.cpp.
Can the DGX Spark and Mac Studio M3 Ultra run Llama 3.3 70B?
Llama 3.3 70B needs about 46 GB of memory at 4-bit for one user with 8K context. The DGX Spark runs it on one unit at about 7 tokens/s at most for one user, and the Mac Studio M3 Ultra runs it on one unit at about 21 tokens/s at most for one user. Speeds are theoretical ceilings from memory bandwidth; real speeds are usually 50 to 80% of that.
How much does the DGX Spark cost compared with the Mac Studio M3 Ultra?
DGX Spark: US$3,999 at launch (4 TB). Mac Studio M3 Ultra: From US$3,999 (96 GB); more for 256 GB and 512 GB. Data center GPUs are usually bought as part of 4- or 8-GPU servers, so compare full-system quotes.
How are these numbers calculated?
Memory, bandwidth, compute and power are manufacturer specifications. Model memory needs come from our VRAM calculator: weights plus KV cache plus about 5 to 10% runtime overhead, keeping 10% of each GPU free (15% on GB10 systems, 25% on Mac). The speed ceiling is memory bandwidth divided by the model’s weight size. All figures are planning estimates.
Related comparisons
Specifications from the manufacturer pages for the NVIDIA DGX Spark and Apple Mac Studio (2025). Speed ceilings are bandwidth divided by model size, before batching; real throughput depends on the inference engine and settings. Last checked November 10, 2026.
Not sure which hardware fits your team?
Vanik Edge OS runs open-weight models on NVIDIA DGX, AMD Instinct and other GPU servers on your premises, and helps you size the hardware first.