Side-by-side simulation · recommended settings
Direct comparison of local LLM equipment creation speeds
Start the same model at the same moment and check the difference not only in the numbers but also in the speed at which the actual answer is delivered.
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
ReadyMac mini M4 Pro 64GB
Press Start above to generate the same answer on both devices.
Right
ReadyRTX 5090 32GB
Press Start above to generate the same answer on both devices.
With these settings, RTX 5090 32GB finishes the full answer About 21.0× faster.
Compare from input processing to completion of 117 token response.
Which one fits my needs?
Answer completion
RTX 5090 32GB
At the estimate midpoint, it finishes the same answer about 21.0× faster.
Input processing
RTX 5090 32GB
The estimated input-processing midpoint is higher at 13,161 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
MLX 4-bit / vLLM 0.28.0 · CUDA 13.3.1 · driver 610.43.02 · MTP-2. When runtime paths differ, the speed gap also includes software and model-format differences.
Estimated power draw
Mac mini M4 Pro 64GB
Estimated LLM power draw is lower at about 95 W. Noise and physical size need a separate check.
Local pricing
The Mac mini M4 Pro 64GB is a complete system, while the RTX 5090 32GB is a GPU-only unit. The GPU-only price should be compared after adding the base system cost.
Before choosing hardware
| Measure | Mac mini M4 Pro 64GB | RTX 5090 32GB |
|---|---|---|
| Available fast memory | 56GB | 30.5GB |
| Memory bandwidth | 273GB/s | 1792GB/s |
| Estimated total time | 20 s | 1.0 s |
| Estimated sustained LLM load | about 95W | about 620W |
| Korean price range (KRW) | 412–620 × KRW 10,000 | 866–1,209 × KRW 10,000 |
Estimated ranges for one user. Multi-GPU and multi-device results depend on the runtime and interconnect.