Can the RTX 3080 10 GB run Z-Image (base)?
Yes, but only just: Q8_0 leaves 0.8 GB free on the RTX 3080 10 GB.
Q8_0 (7.2 GB) fits with only 0.8 GB to spare (about 9.2 GB of 10 GB).
01What to download
| Part | File | Folder | Size | |
|---|---|---|---|---|
| Model | z-image-Q8_0.gguf Q8_0 · unsloth/Z-Image-GGUF | models/unet | 7.2 GB | Download → |
| Text encoder | qwen_3_4b_fp8_mixed.safetensors Qwen3 4B FP8 · Comfy-Org/z_image | models/text_encoders | 5.6 GB | Download → |
| VAE | ae.safetensors FLUX.1 VAE (ae) · Comfy-Org/z_image | models/vae | 0.3 GB | Download → |
| Total download · keep about the same free on disk | 13.2 GB | |||
Alternatives: Qwen3 4B BF16 (8.0 GB). Sizes from Hugging Face, 2026-10-01.
Free ComfyUI workflow, tested on my RTX 5060 Ti: 16-bit workflow (.json). Drag the file into ComfyUI. All workflows →
02Every Z-Image file on 10 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| 16-bit SAFETENSORS · Comfy-Org | 12.3 GB | 14.3 GB | 4.3 GB too big | the original weights | Hugging Face → |
| Q8_0 ← GGUF · unsloth | 7.2 GB | 9.2 GB | fits · 0.8 GB spare | practically identical to the original | Hugging Face → |
| INT8 SAFETENSORS · Comfy-Org | 6.2 GB | 8.2 GB | fits · 1.8 GB spare | practically identical to the original | Hugging Face → |
| Q6_K GGUF · unsloth | 6.1 GB | 8.1 GB | fits · 1.9 GB spare | very close to the original | Hugging Face → |
| Q5_K_M GGUF · unsloth | 5.6 GB | 7.6 GB | fits · 2.4 GB spare | close; small differences in fine detail | Hugging Face → |
| Q4_K_M GGUF · unsloth | 5.1 GB | 7.1 GB | fits · 2.9 GB spare | good; some loss in fine detail and text | Hugging Face → |
| Q3_K_M GGUF · unsloth | 4.6 GB | 6.6 GB | fits · 3.4 GB spare | noticeable loss of detail | Hugging Face → |
| Q2_K GGUF · unsloth | 4.0 GB | 6.0 GB | fits · 4.0 GB spare | heavy loss; a last resort | Hugging Face → |
“Needed” adds 1.2 GB of working memory for Z-Image 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 Z-Image on a RTX 3080 10 GB, send your time per image and peak VRAM and it will appear here, credited.
04About 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 →
05Questions
How much VRAM does Z-Image need?
Around 6.0 GB with the smallest file (Q2_K) and 8.2 GB with an 8-bit file (INT8). The full 16-bit file needs about 14.3 GB.
Which Z-Image file should I download for the RTX 3080 10 GB?
Q8_0 (7.2 GB) from unsloth/Z-Image-GGUF. Load it with Unet Loader (GGUF) from the ComfyUI-GGUF node pack.
How much do I need to download for Z-Image on the RTX 3080 10 GB?
13.2 GB: z-image-Q8_0.gguf, qwen_3_4b_fp8_mixed.safetensors, ae.safetensors.