Used cost-effectiveness · Officially released product · Updated 2026-09-06
If you are choosing a used 24GB GPU, also check the power and condition of the product.
RTX 3090 24GB local LLM used cost-effectiveness and performance
The price is very competitive when you need 24GB VRAM and CUDA and can check the used condition directly.
Core specifications
- available memory
- 22.5GB
- Memory bandwidth
- 936GB/s
- local LLM sustained load
- about 420W
- Korean price range (KRW)
- 165–220 × KRW 10,000
a person who fits well
- Introduction to 24GB CUDA
- 27B Q4 Inference
- Create ComfyUI image
Check before purchasing
High power, heat generation, used memory status, power, and case cost must be calculated along with the graphics card price.
What tasks is it suitable for?
| task | determination | reason |
|---|---|---|
| 27B conversation | fit | As of Q4, you can aim for both loading and CUDA acceleration. |
| Create image | comfortable | Its strengths include 24GB VRAM and a wide CUDA ecosystem. |
| 24 hour server | Not recommended | Power and heat generation are greater than low-power finished products. |
Configuration Selection Criteria
The first bottleneck encountered
The 24GB VRAM boundary and high power consumption determine long-term operating costs.
Recommended purchase setup
RTX 3090 24GB · System RAM 64GB · 850W or more power · Sufficient cooling
Criteria for spending more money
If you need 24GB, compare the price difference with a new product. If you want less power and warranty, compare the new 16GB or the finished product.
4K context · Q4 model-specific expected performance
| Model | required memory | Decode | First token | Prefill |
|---|---|---|---|---|
| Gemma 4 12B llama.cpp CUDA · Q4_K_M | 8GB | 95.5 ~ 106.1 tok/s | 2.6 ~ 3.3 s | 1,258 ~ 1,601 tok/s |
| Qwen3.8 27B llama.cpp CUDA · Q4_K_M | 17.56GB | 42.3 ~ 46.9 tok/s | 5.0 ~ 6.4 s | 645 ~ 821 tok/s |
| Qwen3.8-Flash-Next llama.cpp CUDA · Q4_K_M | 77.01GB | 6.3 ~ 6.9 tok/s | 4.7 ~ 16.1 s | 257 ~ 892 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 a local LLM using an RTX 3090 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 3090 24GB?
Expected to be around 420W under sustained load, with an actual range of 330–540W.
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.