Mac Studio vs DGX Spark: MLX or CUDA?
Two desktop machines with 128 GB of unified memory, built on very different bets. How the Mac Studio M5 Max and the NVIDIA DGX Spark compare on bandwidth, compute, software and tooling, and when to pick which.
5 min read
- Mac Studio
- DGX Spark
- MLX
- CUDA
On this page
On paper, the Mac Studio M5 Max and the NVIDIA DGX Spark look like rivals: two compact desktop machines, each with 128 GB of memory shared between CPU and GPU, both aimed at people who want to run large AI models locally. In practice they're built on different bets — Apple on memory bandwidth, NVIDIA on compute and CUDA — and they suit different work.
Side by side
| Mac Studio M5 Max | NVIDIA DGX Spark | |
|---|---|---|
| Chip | Apple M5 Max | NVIDIA GB10 Grace Blackwell |
| CPU | 18 cores (Arm) | 20 cores (Arm) |
| GPU | 40-core Apple GPU | Blackwell GPU, 5th-gen Tensor Cores |
| Unified memory | 128 GB | 128 GB LPDDR5x |
| Memory bandwidth | 614 GB/s | 273 GB/s |
| Peak AI compute | lower | up to 1 PFLOP (FP4, sparse) |
| Operating system | macOS | DGX OS (Ubuntu) |
| Main framework | MLX, Metal | CUDA |
| Containers | not native to macOS | Docker with GPU access |
| Linking two | no | yes, via ConnectX-7 |
The two bold entries are the heart of the comparison.
Bandwidth: the Mac generates faster
When a model generates text, each token reads the model's active weights from memory, so generation speed follows memory bandwidth. The M5 Max has about 2.2 times the DGX Spark's bandwidth, so for the same model at the same precision it has about 2.2 times the speed ceiling:
| Model (4-bit) | Size | Mac Studio M5 Max | DGX Spark |
|---|---|---|---|
| 8B dense | ~4.6 GB | ~130 tokens/s | ~60 tokens/s |
| 32B dense | ~18 GB | ~33 tokens/s | ~15 tokens/s |
| 70B dense | ~40 GB | ~15 tokens/s | ~7 tokens/s |
Ceilings from bandwidth ÷ model size; real speeds are lower on both.
For one person chatting with, coding with, or evaluating a large dense model, that difference is what you feel most.
Compute: the Spark reads, batches and trains faster
The DGX Spark's Blackwell GPU has far more matrix-maths throughput, especially at the low precisions (FP8, FP4) its Tensor Cores support natively. Compute-bound work favours it:
- Prompt processing. Before answering, a model reads your whole prompt in one large batch. Long documents, big code files and retrieval-augmented prompts start answering sooner on the Spark.
- Serving many requests at once. Batching several users' requests turns bandwidth-bound generation into compute-bound work, where the Spark scales better. Serving engines like vLLM and SGLang are built for this.
- Fine-tuning. Training passes are compute-heavy, and the CUDA ecosystem's fine-tuning tools are the most mature.
The M5 generation narrows the gap for prompt processing — its GPU cores include Neural Accelerators for matrix maths — but for heavy batch work and training the Spark stays ahead.
Software: MLX or CUDA
This is often the deciding factor.
On the Mac Studio, the native path is Apple's MLX, with mlx-lm for language models, mlx-vlm for vision-language models, and thousands of ready-converted models from the mlx-community organisation on Hugging Face. llama.cpp and tools built on it run on Metal too, and PyTorch has an mps backend for the GPU, though not every operation is supported. Setup is a Python virtual environment and pip install. See Running Qwen3 and Llama with MLX.
On the DGX Spark, you get the whole CUDA world: PyTorch with full GPU support, vLLM, SGLang, TensorRT-LLM, NVIDIA NIM microservices and NGC containers, plus any library with custom CUDA kernels. If a paper's code says "requires CUDA", it runs here. The same software runs on NVIDIA's data-centre systems, so work moves up to a cluster without porting. One caveat: the CPU is Arm64, so you need Arm64 builds and containers, which the major projects provide.
Model formats and quantisation
- Mac: MLX's own 4-bit and 8-bit formats (with per-group scales), bf16, and GGUF through llama.cpp. You can convert and quantise any Hugging Face model with
mlx_lm.convert. - Spark: the formats of the CUDA world — FP8, NVIDIA's NVFP4, MXFP4 (used by OpenAI's gpt-oss models), AWQ and GPTQ, plus GGUF through llama.cpp's CUDA backend. Blackwell runs FP4 and FP8 in hardware.
Both machines hold the same sizes of model — up to 70B dense at 4-bit with long contexts, or mixture-of-experts models such as gpt-oss-120b. The difference is how fast they run them, not whether they fit.
Working day to day
- Mac Studio: a Unix shell on macOS. No Docker GPU access, so environments are virtualenvs, not containers. Tools that assume Linux may need adjusting.
- DGX Spark: a standard Ubuntu-based Linux with Docker. If your workflow is containers and
docker run --gpus all, you're at home.
Which to pick
Pick the Mac Studio if:
- you mostly generate text with large dense models, one user at a time;
- tokens per second on 30B–70B models matter most;
- your stack is Python and MLX, llama.cpp, or anything that runs on Metal.
Pick the DGX Spark if:
- your code or frameworks need CUDA;
- you fine-tune, train or process long prompts in bulk;
- you serve several requests at once with vLLM or SGLang;
- you're developing for NVIDIA's data-centre hardware and want the same stack locally;
- you want mixture-of-experts models with FP4/FP8 support in hardware.
Or use both. They complement each other: a Mac Studio for fast interactive generation and evaluation, a DGX Spark for CUDA work, fine-tuning and batch jobs. Renting by the day makes that practical — you don't need to own both to use the right one for each job.
On GpuToLease.ai
The Mac Studio M5 Max with 128 GB is available to reserve by the day now. DGX Spark machines are coming soon; see the pricing page for what's on the way, and DGX Spark and the GB10, explained for a closer look at NVIDIA's machine.