Can the Arc B580 12 GB run Wan 2.1 T2V 14B?
Not at full quality. The 16-bit and 8-bit files are too big for the Arc B580 12 GB; the largest that fits is Q3_K_M (7.6 GB).
Q3_K_M (7.6 GB) fits with only 0.1 GB to spare (about 11.9 GB of 12 GB). Quality: noticeable loss of detail.
Using Kijai's WanVideoWrapper instead of the native nodes? Its WanVideo BlockSwap node keeps part of the model in system RAM. To run the FP8 file (14.3 GB) on Arc B580 12 GB, start with blocks_to_swap ≈ 20 (of 40). Each block is about 0.4 GB; raise the number if you still run out of memory, lower it for speed. estimate WanVideoWrapper →
01What to download
| Part | File | Folder | Size | |
|---|---|---|---|---|
| Model | wan2.1-t2v-14b-Q3_K_M.gguf Q3_K_M · city96/Wan2.1-T2V-14B-gguf | models/unet | 7.6 GB | Download → |
| Text encoder | umt5_xxl_fp8_e4m3fn_scaled.safetensors UMT5-XXL FP8 (scaled) · Comfy-Org/Wan_2.1_ComfyUI_repackaged | models/text_encoders | 6.7 GB | Download → |
| VAE | wan_2.1_vae.safetensors Wan 2.1 VAE · Comfy-Org/Wan_2.1_ComfyUI_repackaged | models/vae | 0.3 GB | Download → |
| Total download · keep about the same free on disk | 14.6 GB | |||
Alternatives: UMT5-XXL FP16 (11.4 GB). Sizes from Hugging Face, 2026-10-01.
02Every Wan 2.1 14B file on 12 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| 16-bit SAFETENSORS · Comfy-Org | 28.6 GB | 32.9 GB | 20.9 GB too big | the original weights | Hugging Face → |
| Q8_0 GGUF · city96 | 15.9 GB | 20.2 GB | 8.2 GB too big | practically identical to the original | Hugging Face → |
| FP8 SAFETENSORS · Comfy-Org | 14.3 GB | 18.6 GB | 6.6 GB too big | practically identical to the original | Hugging Face → |
| Q6_K GGUF · city96 | 12.5 GB | 16.8 GB | 4.8 GB too big | very close to the original | Hugging Face → |
| Q5_K_M GGUF · city96 | 11.3 GB | 15.6 GB | 3.6 GB too big | close; small differences in fine detail | Hugging Face → |
| Q4_K_M GGUF · city96 | 10.1 GB | 14.4 GB | 2.4 GB too big | good; some loss in fine detail and text | Hugging Face → |
| Q3_K_M ← GGUF · city96 | 7.6 GB | 11.9 GB | fits · 0.1 GB spare | noticeable loss of detail | Hugging Face → |
“Needed” adds 3.5 GB of working memory for Wan 2.1 14B 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 Wan 2.1 14B on a Arc B580 12 GB, send your time per image and peak VRAM and it will appear here, credited.
04With more VRAM
With 20 GB you could run Wan 2.1 14B at 8-bit or better (FP8, 14.3 GB) with no offloading.
What changes from the Arc B580 12 GB to the RTX 5060 Ti 16 GB →
05About the Arc B580 12 GB
Intel Arc GPUs run ComfyUI through PyTorch's native XPU support on Windows 11 and Linux. Memory works the same as on NVIDIA, so the fit verdicts apply. FP8 files save memory but give no speed-up, and some custom nodes are NVIDIA-only.
12 GB GDDR6 · 192-bit · 456 GB/s · Battlemage. Everything that runs on it →
06Questions
How much VRAM does Wan 2.1 14B need?
Around 11.9 GB with the smallest file (Q3_K_M) and 18.6 GB with an 8-bit file (FP8). The full 16-bit file needs about 32.9 GB.
Which Wan 2.1 14B file should I download for the Arc B580 12 GB?
Q3_K_M (7.6 GB) from city96/Wan2.1-T2V-14B-gguf. Load it with Unet Loader (GGUF) from the ComfyUI-GGUF node pack.
FP8 or Q8_0 for Wan 2.1 14B on the Arc B580 12 GB?
Q8_0 (15.9 GB). The Arc B580 12 GB has no FP8 maths, so FP8 (14.3 GB) only saves memory and is a step further from the original.
How much do I need to download for Wan 2.1 14B on the Arc B580 12 GB?
14.6 GB: wan2.1-t2v-14b-Q3_K_M.gguf, umt5_xxl_fp8_e4m3fn_scaled.safetensors, wan_2.1_vae.safetensors.