Can the RTX 4090 24 GB run Krea 2 (Turbo)?
Yes. FP8 fits on the RTX 4090 24 GB with 8.3 GB to spare.
FP8 (13.1 GB) needs about 15.7 GB in use, so 8.3 GB of the 24 GB stays free: room for a larger image, a LoRA or a ControlNet.
01What to download
| Part | File | Folder | Size | |
|---|---|---|---|---|
| Model | krea2_turbo_fp8_scaled.safetensors FP8 · Comfy-Org/Krea-2 | models/diffusion_models | 13.1 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 | 18.6 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 24 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| 16-bit SAFETENSORS · Comfy-Org | 26.3 GB | 28.9 GB | 4.9 GB too big | the original weights | Hugging Face → |
| FP8 ← SAFETENSORS · Comfy-Org | 13.1 GB | 15.7 GB | fits · 8.3 GB spare | practically identical to the original | Hugging Face → |
| INT8 SAFETENSORS · Comfy-Org | 13.5 GB | 16.1 GB | fits · 7.9 GB spare | practically identical to the original | Hugging Face → |
| Q8_0 GGUF · vantagewithai | 13.7 GB | 16.3 GB | fits · 7.7 GB spare | practically identical to the original | Hugging Face → |
| Q6_K GGUF · vantagewithai | 10.6 GB | 13.2 GB | fits · 10.8 GB spare | very close to the original | Hugging Face → |
| Q5_K_M GGUF · vantagewithai | 8.9 GB | 11.5 GB | fits · 12.5 GB spare | close; small differences in fine detail | Hugging Face → |
| Q4_K_M GGUF · vantagewithai | 7.5 GB | 10.1 GB | fits · 13.9 GB spare | good; some loss in fine detail and text | Hugging Face → |
| Q3_K_M GGUF · vantagewithai | 6.0 GB | 8.6 GB | fits · 15.4 GB spare | noticeable loss of detail | Hugging Face → |
| Q2_K GGUF · vantagewithai | 4.9 GB | 7.5 GB | fits · 16.5 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 4090 24 GB, send your time per image and peak VRAM and it will appear here, credited.
04About the RTX 4090 24 GB
The RTX 4090 24 GB is an Ada Lovelace GPU with hardware FP8, so ComfyUI can compute Comfy-Org's FP8 files natively: small and fast. (Plain FP8 files use FP8 maths with the --fast fp8_matrix_mult option.)
24 GB GDDR6X · 384-bit · 1008 GB/s · Ada Lovelace. Everything that runs on it →
05Questions
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 4090 24 GB?
FP8 (13.1 GB) from Comfy-Org/Krea-2.
FP8 or Q8_0 for Krea 2 on the RTX 4090 24 GB?
FP8 (13.1 GB): the RTX 4090 24 GB computes it natively, while Q8_0 (13.7 GB) is unpacked on the fly.
How much do I need to download for Krea 2 on the RTX 4090 24 GB?
18.6 GB: krea2_turbo_fp8_scaled.safetensors, qwen3vl_4b_fp8_scaled.safetensors, qwen_image_vae.safetensors.