Side-by-side simulation · recommended settings
DGX Spark vs M5 Ultra: Which machine for large local LLMs?
Compare DGX Spark to keep CUDA tools, or M5 Ultra for more unified memory and a Mac workflow. Separate speed on a model that fits both from models that fit only one.
Choose devices and a model
Expand comparison settings
Same 117output tokens. Only evidence-qualified acceleration is selected automatically; runtime formats are shown on each device.
Left
ReadyDGX Spark 128GB
Press Start above to generate the same answer on both devices.
Right
ReadyStudio M5 Ultra 256GB
Press Start above to generate the same answer on both devices.
With these settings, Studio M5 Ultra 256GB finishes the full answer About 6.0× faster.
Compare from input processing to completion of 117 token response.
Which one fits my needs?
Answer completion
Studio M5 Ultra 256GB
At the estimate midpoint, it finishes the same answer about 6.0× faster.
Prompt processing
Studio M5 Ultra 256GB
The estimated input-processing midpoint is higher at 4,949 tok/s.
Image and video generation
Check the media model
LLM speed does not tell you media-generation performance. Choose the image or video model and resolution separately.
Runtime
Settings for your hardware
llama.cpp · Q4_K_M / oMLX 0.7.0 (4d4f5a28) · mlx-lm 94cdcae · mlx 0.32.2 · native kernels · Lightning MTP on. When runtime paths differ, the speed gap also includes software and model-format differences.
Estimated power draw
DGX Spark 128GB
Estimated LLM power draw is lower at about 190 W. Noise and physical size need a separate check.
Local pricing
Studio M5 Ultra 256GB costs about 934 × KRW 10,000 more at the reference-price midpoint, with an estimated answer time about 83% shorter.
Before choosing hardware
| Measure | DGX Spark 128GB | Studio M5 Ultra 256GB |
|---|---|---|
| Available fast memory | 116GB | 236GB |
| Memory bandwidth | 273GB/s | 1200GB/s |
| Estimated total time | 9.8 s | 1.6 s |
| Estimated sustained LLM load | about 190W | about 300W |
| Korean price range (KRW) | 952–1,030 × KRW 10,000 | 1,850–2,000 × KRW 10,000 |
Estimated ranges for one user. Multi-GPU and multi-device results depend on the runtime and interconnect.
Check the environment required by your tools
Prepare DGX Spark around NVIDIA software and CUDA, and Mac around MLX or Metal paths. The same model can require different files and server setup. Check your app's server connection, packages and CPU-architecture support first to avoid software obstacles after buying.
Check 128GB versus 256GB with the actual file
Large models need workspace and conversation cache beyond weights. Choose your quantized file and long input, then compare headroom. Check speed on a file that fits Spark first, then consider why you would use a larger file on Mac. This shows whether the extra spending is for capacity.
Separate model serving from everyday computing
Decide between editing and inference on one Mac or using a separate Spark server. Separation frees memory on the work PC but adds connections and management. One machine is simpler but shares memory with other apps. Include that operating model alongside hardware prices.