Can the RTX 3080 10 GB run Krea 2 (Turbo)?
Not at full quality. The 16-bit and 8-bit files are too big for the RTX 3080 10 GB; the largest that fits is Q3_K_M (6.0 GB).
Q3_K_M (6.0 GB) fits with only 1.4 GB to spare (about 8.6 GB of 10 GB). Quality: noticeable loss of detail. For better quality, Q4_K_M (7.5 GB) also runs, with about 0.1 GB spilling into system RAM - a little slower, still practical.
Also try the FP8 file (13.1 GB) on this GPU. It does not fit entirely, but a current ComfyUI streams the overflow from RAM and native 8-bit files often beat a small GGUF that does fit. Dynamic VRAM →
01What to download
| Part | File | Folder | Size | |
|---|---|---|---|---|
| Model | krea2_turbo-Q3_K_M.gguf Q3_K_M · vantagewithai/Krea-2-Turbo-GGUF | models/unet | 6.0 GB | Download → |
| Text encoder | qwen3vl_4b_fp8_scaled.safetensors Qwen3-VL 4B FP8 · Comfy-Org/Krea-2 | models/text_encoders | 5.2 GB | Download → |
| VAE | qwen_image_vae.safetensors Qwen-Image VAE · Comfy-Org/Krea-2 | models/vae | 0.3 GB | Download → |
| Total download · keep about the same free on disk | 11.5 GB | |||
Alternatives: Qwen3-VL 4B BF16 (8.9 GB). Sizes from Hugging Face, 2026-10-01.
Free ComfyUI workflow, tested on my RTX 5060 Ti: INT8 workflow (.json). Drag the file into ComfyUI. All workflows →
02Every Krea 2 file on 10 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| 16-bit SAFETENSORS · Comfy-Org | 26.3 GB | 28.9 GB | 18.9 GB too big | the original weights | Hugging Face → |
| Q8_0 GGUF · vantagewithai | 13.7 GB | 16.3 GB | 6.3 GB too big | practically identical to the original | Hugging Face → |
| FP8 SAFETENSORS · Comfy-Org | 13.1 GB | 15.7 GB | 5.7 GB too big | practically identical to the original | Hugging Face → |
| INT8 SAFETENSORS · Comfy-Org | 13.5 GB | 16.1 GB | 6.1 GB too big | practically identical to the original | Hugging Face → |
| Q6_K GGUF · vantagewithai | 10.6 GB | 13.2 GB | 3.2 GB too big | very close to the original | Hugging Face → |
| Q5_K_M GGUF · vantagewithai | 8.9 GB | 11.5 GB | spills 1.5 GB | close; small differences in fine detail | Hugging Face → |
| Q4_K_M GGUF · vantagewithai | 7.5 GB | 10.1 GB | spills 0.1 GB | good; some loss in fine detail and text | Hugging Face → |
| Q3_K_M ← GGUF · vantagewithai | 6.0 GB | 8.6 GB | fits · 1.4 GB spare | noticeable loss of detail | Hugging Face → |
| Q2_K GGUF · vantagewithai | 4.9 GB | 7.5 GB | fits · 2.5 GB spare | heavy loss; a last resort | Hugging Face → |
“Needed” adds 1.8 GB of working memory for Krea 2 and a 0.8 GB reserve. Sizes 2026-10-01.
03Measured and reported results
Nobody has sent measured numbers for this pair yet. If you run Krea 2 on a RTX 3080 10 GB, send your time per image and peak VRAM and it will appear here, credited.
04With more VRAM
With 16 GB you could run Krea 2 at 8-bit or better (FP8, 13.1 GB) with no offloading: for example on the RTX 5080 16 GB, RTX 5070 Ti 16 GB, RTX 5060 Ti 16 GB.
What changes from the RTX 3080 10 GB to the RTX 3090 24 GB → · What changes from the RTX 3080 10 GB to the RTX 5070 Ti 16 GB →
05About the RTX 3080 10 GB
The RTX 3080 10 GB (Ampere) has no FP8 compute. FP8 files still load and save memory, but ComfyUI converts them back before the maths, so there is no speed gain - Q8_0 GGUF is the closer-to-original 8-bit choice. ComfyUI can compute INT8 files natively on NVIDIA, which is worth a try where a model offers one.
10 GB GDDR6X · 320-bit · 760 GB/s · Ampere. Everything that runs on it →
06Questions
How much VRAM does Krea 2 need?
Around 7.5 GB with the smallest file (Q2_K) and 15.7 GB with an 8-bit file (FP8). The full 16-bit file needs about 28.9 GB.
Which Krea 2 file should I download for the RTX 3080 10 GB?
Q3_K_M (6.0 GB) from vantagewithai/Krea-2-Turbo-GGUF. Load it with Unet Loader (GGUF) from the ComfyUI-GGUF node pack.
FP8 or Q8_0 for Krea 2 on the RTX 3080 10 GB?
Q8_0 (13.7 GB). The RTX 3080 10 GB has no FP8 maths, so FP8 (13.1 GB) only saves memory and is a step further from the original.
How much do I need to download for Krea 2 on the RTX 3080 10 GB?
11.5 GB: krea2_turbo-Q3_K_M.gguf, qwen3vl_4b_fp8_scaled.safetensors, qwen_image_vae.safetensors.