Can the RX 7900 XTX 24 GB run Krea 2 (Turbo)?
Yes. Q8_0 fits on the RX 7900 XTX 24 GB with 7.7 GB to spare.
Q8_0 (13.7 GB) needs about 16.3 GB in use, so 7.7 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-Q8_0.gguf Q8_0 · vantagewithai/Krea-2-Turbo-GGUF | models/unet | 13.7 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 | 19.2 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 → |
| Q8_0 ← GGUF · vantagewithai | 13.7 GB | 16.3 GB | fits · 7.7 GB spare | practically identical to the original | 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 → |
| 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 RX 7900 XTX 24 GB, send your time per image and peak VRAM and it will appear here, credited.
04About the RX 7900 XTX 24 GB
ComfyUI supports AMD GPUs on Windows officially since v0.7.0 (January 2026), through ROCm. Memory works the same as on NVIDIA, so the fit verdicts apply. Some custom nodes are NVIDIA-only. RDNA 3 has no FP8 compute, so FP8 files save memory but give no speed-up.
24 GB GDDR6 · 384-bit · 960 GB/s · RDNA 3. 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 RX 7900 XTX 24 GB?
Q8_0 (13.7 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 RX 7900 XTX 24 GB?
Q8_0 (13.7 GB). The RX 7900 XTX 24 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 RX 7900 XTX 24 GB?
19.2 GB: krea2_turbo-Q8_0.gguf, qwen3vl_4b_fp8_scaled.safetensors, qwen_image_vae.safetensors.