Can the Arc B580 12 GB run Wan 2.2 TI2V 5B?
Yes, but only just: Q8_0 leaves 2.8 GB free on the Arc B580 12 GB.
Take Q8_0 (5.4 GB). In use it comes to about 9.2 GB, with 2.8 GB left over on 12 GB.
Using Kijai's WanVideoWrapper instead of the native nodes? Its WanVideo BlockSwap node keeps part of the model in system RAM. To run the 16-bit file (10.0 GB) on Arc B580 12 GB, start with blocks_to_swap ≈ 7 (of 30). Each block is about 0.3 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 | Wan2.2-TI2V-5B-Q8_0.gguf Q8_0 · QuantStack/Wan2.2-TI2V-5B-GGUF | models/unet | 5.4 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 | wan2.2_vae.safetensors Wan 2.2 VAE · Comfy-Org/Wan_2.2_ComfyUI_Repackaged | models/vae | 1.4 GB | Download → |
| Total download · keep about the same free on disk | 13.5 GB | |||
Alternatives: UMT5-XXL FP16 (11.4 GB). Sizes from Hugging Face, 2026-10-01.
Free ComfyUI workflow, tested on my RTX 5060 Ti: text to video workflow (.json). Drag the file into ComfyUI. All workflows →
02Every Wan 2.2 5B file on 12 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| 16-bit SAFETENSORS · Comfy-Org | 10.0 GB | 13.8 GB | spills 1.8 GB | the original weights | Hugging Face → |
| Q8_0 ← GGUF · QuantStack | 5.4 GB | 9.2 GB | fits · 2.8 GB spare | practically identical to the original | Hugging Face → |
| Q6_K GGUF · QuantStack | 4.2 GB | 8.0 GB | fits · 4.0 GB spare | very close to the original | Hugging Face → |
| Q5_K_M GGUF · QuantStack | 3.8 GB | 7.6 GB | fits · 4.4 GB spare | close; small differences in fine detail | Hugging Face → |
| Q4_K_M GGUF · QuantStack | 3.4 GB | 7.2 GB | fits · 4.8 GB spare | good; some loss in fine detail and text | Hugging Face → |
| Q3_K_M GGUF · QuantStack | 2.5 GB | 6.3 GB | fits · 5.7 GB spare | noticeable loss of detail | Hugging Face → |
| Q2_K GGUF · QuantStack | 1.9 GB | 5.7 GB | fits · 6.3 GB spare | heavy loss; a last resort | Hugging Face → |
“Needed” adds 3 GB of working memory for Wan 2.2 5B and a 0.8 GB reserve. Sizes 2026-10-01.
03Measured and reported results
| Label | Setup | Result | Peak VRAM | Date | Source |
|---|---|---|---|---|---|
| reported | Wan 2.2 5B T2V (template) · 640x352 “Wan 2.2 5B Text to Video (640x352, 121 frames): FAST (60s, very poor quality)” Arc B580 12GB, PyTorch 2.8 XPU; same post: 960x544/121f = 181s, 1280x704 OOM; steps not stated | 60 s / clip (121 frames) | — | 2025-08-03 | github.com → |
Other people's numbers are copied as published, with a link. Send yours.
04About 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 →
05Questions
How much VRAM does Wan 2.2 5B need?
Around 5.7 GB with the smallest file (Q2_K) and 9.2 GB with an 8-bit file (Q8_0). The full 16-bit file needs about 13.8 GB.
Which Wan 2.2 5B file should I download for the Arc B580 12 GB?
Q8_0 (5.4 GB) from QuantStack/Wan2.2-TI2V-5B-GGUF. Load it with Unet Loader (GGUF) from the ComfyUI-GGUF node pack.
How much do I need to download for Wan 2.2 5B on the Arc B580 12 GB?
13.5 GB: Wan2.2-TI2V-5B-Q8_0.gguf, umt5_xxl_fp8_e4m3fn_scaled.safetensors, wan2.2_vae.safetensors.