24GB high-speed · Officially released product · Updated 2026-09-06
Anything that fits into 24GB is fast. The limit is also exactly 24GB.
RTX 4090 24GB local LLM and image creation performance
This is the machine to look at if you want to quickly process LLM and image creation that fit in 24GB and avoid the new high price.
Core specifications
- available memory
- 22.5GB
- Memory bandwidth
- 1008GB/s
- local LLM sustained load
- about 470W
- Korean price range (KRW)
- 348–385 × KRW 10,000
a person who fits well
- Fast 12B to 27B inference
- Create ComfyUI image
- CUDA development environment
Check before purchasing
Models with more than 24GB cannot be loaded even if they are fast, and their appeal diminishes if used prices are close to the 5090.
What tasks is it suitable for?
| task | determination | reason |
|---|---|---|
| 27B conversation | comfortable | High single GPU speed within 24GB. |
| Create image | comfortable | Compare the processing speed of the same model together with our support tools. |
| large model | conditional | If you exceed VRAM, CPU/RAM offloading loss is significant. |
Configuration Selection Criteria
The first bottleneck encountered
The 24GB VRAM limit is reached before compute performance.
Recommended purchase setup
RTX 4090 24GB · System RAM 64GB · 1000W power · 4 slots available
Criteria for spending more money
It's worth it when the price difference over the 5090 is big enough and 24GB is enough. If you need 32GB, look no further than the 5090.
4K context · Q4 model-specific expected performance
| Model | required memory | Decode | First token | Prefill |
|---|---|---|---|---|
| Gemma 4 12B llama.cpp CUDA · Q4_K_M | 8GB | 107.1 ~ 118.5 tok/s | 1.5 ~ 1.6 s | 2,652 ~ 2,733 tok/s |
| Qwen3.8 27B llama.cpp CUDA · Q4_K_M | 17.56GB | 44.1 ~ 48.7 tok/s | 2.9 ~ 3.0 s | 1,360 ~ 1,401 tok/s |
| Qwen3.8-Flash-Next llama.cpp CUDA · Q4_K_M | 77.01GB | 6.3 ~ 6.9 tok/s | 2.8 ~ 7.7 s | 542 ~ 1,523 tok/s (NVMe cold~warm) |
Single-user expected range and may vary depending on runtime, cooling, and quantization files.
Frequently Asked Questions
What LLM can be run on an RTX 4090 24GB?
Gemma 4 12B, Qwen3.8 27B, Qwen3.8-Flash-Next, etc. can be compared with 4K input and recommended formats per device. The required memory varies depending on the model and context length.
What is the power consumption of a local LLM on an RTX 4090 with 24GB?
Expected to consume approximately 470W under sustained load, with an actual range estimated between 350–600W.
Are the displayed token speeds ground truth?
This is an estimated range calibrated to the calculator against official hardware specifications and verified benchmark ranges. Runtime, cooling, quantization may vary depending on file and context length.