Side-by-side simulation · recommended settings
DGX Spark 1 vs 2 Local LLM Serving Comparison
The two-machine configuration has two 128GB nodes rather than a single 256GB memory. This is advantageous when larger models and throughput headroom are the goal, and the rate of a single request is not immediately doubled.
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
Ready2× DGX Spark 256GB
Press Start above to generate the same answer on both devices.
With these settings, 2× DGX Spark 256GB finishes the full answer About 1.2× faster.
Compare from input processing to completion of 117 token response.
Which one fits my needs?
Answer completion
2× DGX Spark 256GB
At the estimate midpoint, it finishes the same answer about 1.2× faster.
Prompt processing
2× DGX Spark 256GB
The estimated input-processing midpoint is higher at 1,745 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 / 4-bit · 2-node setup. 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
2× DGX Spark 256GB costs about 991 × KRW 10,000 more at the reference-price midpoint, with an estimated answer time about 17% shorter.
Before choosing hardware
| Measure | DGX Spark 128GB | 2× DGX Spark 256GB |
|---|---|---|
| Available fast memory | 116GB | 232GB |
| Memory bandwidth | 273GB/s | 546GB/s |
| Estimated total time | 9.8 s | 8.2 seconds |
| Estimated sustained LLM load | about 190W | about 380W |
| Korean price range (KRW) | 952–1,030 × KRW 10,000 | 1,904–2,060 × KRW 10,000 |
Estimated ranges for one user. Multi-GPU and multi-device results depend on the runtime and interconnect.