The Two Local-AI Workstations Developers Compare
NVIDIA DGX Spark (formerly Project DIGITS, announced at CES 2025) and the Apple Mac Studio with M5 Max are the two compact machines developers most often weigh for local LLM work. Both offer 128GB of unified memory. DGX Spark costs $4,699, and a 128GB M5 Max Mac Studio is reported at $4,799 to $5,400. The short version: the Mac has more memory bandwidth and should be faster at generating tokens on large dense models, while DGX Spark has the CUDA software stack, faster prompt processing, fine-tuning support and clustering. This page was revised on October 2, 2026 to replace earlier estimates with measured and vendor-published figures.
Spec Comparison
| Spec | NVIDIA DGX Spark | Mac Studio M5 Max (128GB config) |
|---|---|---|
| Compute silicon | GB10 Grace Blackwell Superchip, 20-core Arm CPU and Blackwell GPU | Apple M5 Max, with up to a 40-core GPU |
| Unified memory | 128GB LPDDR5x | 36GB to 128GB |
| Memory bandwidth | 273 GB/s | 460 GB/s, or 614 GB/s with the 40-core GPU |
| AI compute claim | Up to 1 petaflop (NVIDIA) | Not published in comparable terms |
| Storage | 4TB NVMe | Configurable SSD |
| Networking | 10GbE plus ConnectX-7 at 200Gbps | 10GbE |
| Power | 240W power supply, 140W TDP | 480W maximum continuous power for Mac Studio |
| Size | 150 x 150 x 50.5 mm, 1.2 kg | Mac Studio desktop enclosure |
| Price | $4,699 since February 2026, $3,999 at launch | $4,799 to $5,400 reported for 128GB. Mac Studio M5 Max starts at $2,499 |
| OS | NVIDIA DGX OS (Ubuntu-based) | macOS |
| Software stack | CUDA, cuDNN, TensorRT, PyTorch, vLLM | MLX, Metal, PyTorch (MPS backend), llama.cpp |
Specs from the NVIDIA DGX Spark product page and Apple Mac Studio specs. The 128GB Mac price range comes from secondary buyer guides that disagree with each other, so check Apple's configurator.
Measured Inference Speed
We found no published test that runs the same model at the same quantisation on both machines. The table lists what has been measured on each, with who measured it. Figures are tokens per second.
| Device | Model and test | Prompt processing | Generation | Measured by |
|---|---|---|---|---|
| DGX Spark | Llama 3.1 8B Q4_K_M, Ollama | 7,614 | 38.0 | Ollama |
| DGX Spark | gpt-oss 20B, llama.cpp | 2,009 | 60.9 | llama.cpp community |
| DGX Spark | Gemma 3 27B Q4, Ollama | 834 | 10.8 | Ollama |
| DGX Spark | Qwen3 32B, Ollama | 705 | 9.4 | Ollama |
| DGX Spark | Llama 3.1 70B, Ollama | 1,911 | 4.4 | Ollama |
| DGX Spark | gpt-oss 120B, llama.cpp | 1,956 | 60.6, or 40.6 at 32K context | llama.cpp community |
| Mac Studio M5 Max | Llama 2 7B Q4_0, llama.cpp | 3,220 | 119.9 | llama.cpp scoreboard |
| Mac Studio M5 Max 128GB | Qwen3.8-27B, MLX | not given | 65 to 69 | Context Studios, single secondary source |
Two patterns stand out. DGX Spark is slow at generating tokens on dense models above about 27B parameters, because every token reads the whole model through 273 GB/s of memory bandwidth. It is fast on sparse mixture-of-experts models such as gpt-oss, where only part of the model is read per token. The M5 Max has roughly twice the bandwidth, which is why it is expected to lead on dense-model generation, and the one 27B figure we have points that way. DGX Spark's numbers have also improved with software updates since launch.
For wider tables see local LLM tokens-per-second benchmarks.
Fine-Tuning
NVIDIA positions DGX Spark for fine-tuning models of up to 70B parameters and inference on models of up to 200B. The CUDA stack is the practical reason to pick it for training work: most fine-tuning toolchains target CUDA first. Fine-tuning on Apple Silicon is possible through MLX for LoRA-style jobs on smaller models. We have no attributable wall-clock comparison between the two, so this page does not give one.
Power
DGX Spark ships with a 240W power supply and has a 140W TDP. Apple lists 480W as the maximum continuous power for the Mac Studio enclosure, which covers the M5 Ultra as well, and an M5 Max configuration draws less than that under load. Both are quiet desktop machines. Neither is a rack server.
Software Ecosystem
Four practical differences. First, CUDA libraries such as vLLM and TensorRT-LLM ship first on NVIDIA, and MLX follows. Second, DGX Spark units can be linked through ConnectX-7, while Mac Studio has no comparable native clustering fabric. Third, NVIDIA's container ecosystem (NGC, NIM microservices) is NVIDIA-native. Fourth, a Mac Studio doubles as an everyday Mac, which matters for developers who want one machine.
When Each Wins
DGX Spark wins for: CUDA-only code, fine-tuning, long-prompt agent workloads where prompt processing dominates, large sparse models such as gpt-oss 120B, and multi-unit clusters.
Mac Studio M5 Max wins for: generation speed on dense models that need more than 32GB, a single machine that is also a daily workstation, and teams already building on MLX or for Apple platforms.
If you need more than 128GB, the Mac Studio M5 Ultra offers 96GB, 256GB or 512GB. See Mac Studio M5 Ultra benchmarks. For NVIDIA's Windows counterpart see RTX Spark vs DGX Spark.
Brand Visibility Implications
Both devices put large open-weight models within reach of a single developer, which increases the number of people getting answers from a model that never calls a cloud API. Those models answer from training data unless the user adds retrieval, so what an open-weight model learned about a brand is what its users see. For brands tracking AI visibility, the open-weight models that run well on this class of hardware are a growing surface.
Methodology
Specs from the NVIDIA and Apple product pages. DGX Spark measurements from Ollama and the llama.cpp DGX Spark thread. The M5 Max row is from the llama.cpp Apple Silicon scoreboard. DGX Spark pricing is from secondary reviews. None of these are Presenc AI measurements. Last update: October 2026.
How Presenc AI Helps
Presenc AI tracks how AI models describe and recommend your brand, including the open-weight models people run on local hardware. Because local models answer from training data, measuring what they already say about you is the practical way to see this surface.