Side-by-side simulation · recommended settings
RTX 3090 vs RTX 5080: Can you give up 24GB for local AI?
Moving from a 3090 to a 5080 reduces VRAM from 24GB to 16GB. Check whether your model fits the new card first, then compare speed and power for supported combinations.
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
ReadyRTX 3090 24GB
Press Start above to generate the same answer on both devices.
Right
ReadyRTX 5080 16GB
Press Start above to generate the same answer on both devices.
With these settings, RTX 5080 16GB finishes the full answer About 1.0× faster.
Compare from input processing to completion of 117 token response.
Which one fits my needs?
Answer completion
RTX 5080 16GB
At the estimate midpoint, it finishes the same answer about 1.0× faster.
Prompt processing
RTX 5080 16GB
The estimated input-processing midpoint is higher at 2,148 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
Strata 0.1.28 · IQ3_S · MTP · SSD PLE / Strata · Blackwell (CC 12.0) · CUDA · MTP · NVMe PLE · single GPU. When runtime paths differ, the speed gap also includes software and model-format differences.
Estimated power draw
RTX 5080 16GB
Estimated LLM power draw is lower at about 352 W. Noise and physical size need a separate check.
Local pricing
The reference-price midpoints differ by about 4 × KRW 10,000. Check the listed configuration and warranty as well.
Before choosing hardware
| Measure | RTX 3090 24GB | RTX 5080 16GB |
|---|---|---|
| Available fast memory | 22.5GB | 15GB |
| Memory bandwidth | 936GB/s | 960GB/s |
| Estimated total time | 3.3 s | 3.1 s |
| Estimated sustained LLM load | about 420W | About 352W |
| Korean price range (KRW) | 239–239 × KRW 10,000 | 231–239 × KRW 10,000 |
Estimated ranges for one user. Multi-GPU and multi-device results depend on the runtime and interconnect.
Can the new card load your current file?
A 27B quantized file that works on the 3090 may leave too little conversation room on the 5080. Compare loading with the same model and input before replacing it. If you plan a smaller quantization, check answer quality on your usual questions too. Preserving the workload is the first criterion when capacity falls.
Check new runtimes and the existing card's setup
Use each card's supported path rather than applying the 5080's recommendation directly to the 3090. First check whether updating the 3090's runtime reduces waiting. Changing the model here also updates runtime and placement recommendations for both sides; use these labels when choosing files.
If you need 24GB, compare keeping, adding and replacing
If 16GB is regularly insufficient, keeping the 3090 or adding another GPU are options. Splitting a model needs runtime support plus slots, power and cooling. For a single-card replacement, compare the 32GB 5090 too. This helps find a faster path without losing workloads the old card could handle.