Can the RTX 3060 Laptop 6 GB run Krea 2 (Turbo)?
Not entirely. Even Q2_K is 1.5 GB over 6 GB, so ComfyUI keeps part of it in system RAM.
Nothing fits entirely, but Q2_K (4.9 GB) overflows by only about 1.5 GB. ComfyUI keeps that part in system RAM automatically: slower than a full fit, but usable.
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 →
01With 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 3060 Laptop 6 GB to the RTX 5060 Ti 16 GB →
02Every Krea 2 file on 6 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| 16-bit SAFETENSORS · Comfy-Org | 26.3 GB | 28.9 GB | 22.9 GB too big | the original weights | Hugging Face → |
| Q8_0 GGUF · vantagewithai | 13.7 GB | 16.3 GB | 10.3 GB too big | practically identical to the original | Hugging Face → |
| FP8 SAFETENSORS · Comfy-Org | 13.1 GB | 15.7 GB | 9.7 GB too big | practically identical to the original | Hugging Face → |
| INT8 SAFETENSORS · Comfy-Org | 13.5 GB | 16.1 GB | 10.1 GB too big | practically identical to the original | Hugging Face → |
| Q6_K GGUF · vantagewithai | 10.6 GB | 13.2 GB | 7.2 GB too big | very close to the original | Hugging Face → |
| Q5_K_M GGUF · vantagewithai | 8.9 GB | 11.5 GB | 5.5 GB too big | close; small differences in fine detail | Hugging Face → |
| Q4_K_M GGUF · vantagewithai | 7.5 GB | 10.1 GB | 4.1 GB too big | good; some loss in fine detail and text | Hugging Face → |
| Q3_K_M GGUF · vantagewithai | 6.0 GB | 8.6 GB | 2.6 GB too big | noticeable loss of detail | Hugging Face → |
| Q2_K ← GGUF · vantagewithai | 4.9 GB | 7.5 GB | spills 1.5 GB | 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.
03What to download
| Part | File | Folder | Size | |
|---|---|---|---|---|
| Model | krea2_turbo-Q2_K.gguf Q2_K · vantagewithai/Krea-2-Turbo-GGUF | models/unet | 4.9 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 | 10.4 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 →
04The text encoder
At 5.2 GB even the smallest Qwen3-VL 4B file is too big for 6 GB, so it runs from system RAM. That slows down reading the prompt, not the picture. Sizes: 16-bit 8.9 GB, FP8 5.2 GB. Why the encoder does not count against VRAM →
05Measured and reported results
Nobody has sent measured numbers for this pair yet. If you run Krea 2 on a RTX 3060 Laptop 6 GB, send your time per image and peak VRAM and it will appear here, credited.
06About the RTX 3060 Laptop 6 GB
The RTX 3060 Laptop 6 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. As a laptop GPU it runs at a lower power limit than desktop cards (60–115 W depending on the laptop). The memory verdicts are the same; speed depends heavily on how much power the laptop maker allows.
6 GB GDDR6 · 192-bit · Ampere · 60–115 W. Everything that runs on it →
07Questions
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 3060 Laptop 6 GB?
Q2_K (4.9 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 3060 Laptop 6 GB?
Q8_0 (13.7 GB). The RTX 3060 Laptop 6 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 3060 Laptop 6 GB?
10.4 GB: krea2_turbo-Q2_K.gguf, qwen3vl_4b_fp8_scaled.safetensors, qwen_image_vae.safetensors.