Can the RTX 5060 Ti 16 GB run SCAIL-2 (character animation)?
Not at full quality. The 16-bit and 8-bit files are too big for the RTX 5060 Ti 16 GB; the largest that fits is Q3_K_M (9.1 GB).
Q3_K_M (9.1 GB) fits with only 1.6 GB to spare (about 14.4 GB of 16 GB). Quality: noticeable loss of detail. For better quality, Q4_K_M (11.5 GB) also runs, with about 0.8 GB spilling into system RAM - a little slower, still practical.
Also try the INT8 file (16.7 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 →
01What to download
| Part | File | Folder | Size | |
|---|---|---|---|---|
| Model | wan2.1_14B_SCAIL_2-Q3_K_M.gguf Q3_K_M · vantagewithai/SCAIL-2-GGUF-ComfyUI | models/unet | 9.1 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_1_VAE_bf16.safetensors Wan 2.1 VAE BF16 (Kijai) · Kijai/WanVideo_comfy | models/vae | 0.3 GB | Download → |
| Also needed | clip_vision_h.safetensors CLIP Vision H · Comfy-Org/Wan_2.1_ComfyUI_repackaged | models/clip_vision | 1.3 GB | Download → |
| Total download · keep about the same free on disk | 17.4 GB | |||
Alternatives: UMT5-XXL FP16 (11.4 GB). Only for some workflows: SAM 3.1 multiplex FP16 (segmentation) (1.7 GB, character-replacement workflow (masking)). Sizes from Hugging Face, 2026-10-01.
02Every SCAIL-2 file on 16 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| 16-bit SAFETENSORS · Comfy-Org | 32.8 GB | 38.1 GB | 22.1 GB too big | the original weights | Hugging Face → |
| FP8 SAFETENSORS · Comfy-Org | 17.7 GB | 23.0 GB | 7.0 GB too big | practically identical to the original | Hugging Face → |
| INT8 SAFETENSORS · Comfy-Org | 16.7 GB | 22.0 GB | 6.0 GB too big | practically identical to the original | Hugging Face → |
| Q8_0 GGUF · vantagewithai | 18.1 GB | 23.4 GB | 7.4 GB too big | practically identical to the original | Hugging Face → |
| Q6_K GGUF · vantagewithai | 14.2 GB | 19.5 GB | 3.5 GB too big | very close to the original | Hugging Face → |
| Q5_K_M GGUF · vantagewithai | 13.0 GB | 18.3 GB | 2.3 GB too big | close; small differences in fine detail | Hugging Face → |
| Q4_K_M GGUF · vantagewithai | 11.5 GB | 16.8 GB | spills 0.8 GB | good; some loss in fine detail and text | Hugging Face → |
| Q3_K_M ← GGUF · vantagewithai | 9.1 GB | 14.4 GB | fits · 1.6 GB spare | noticeable loss of detail | Hugging Face → |
| Q2_K GGUF · vantagewithai | 7.3 GB | 12.6 GB | fits · 3.4 GB spare | heavy loss; a last resort | Hugging Face → |
“Needed” adds 4.5 GB of working memory for SCAIL-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 SCAIL-2 on a RTX 5060 Ti 16 GB, send your time per image and peak VRAM and it will appear here, credited.
04With more VRAM
With 24 GB you could run SCAIL-2 at 8-bit or better (FP8, 17.7 GB) with no offloading: for example on the RTX 4090 24 GB, RTX 3090 24 GB, RX 7900 XTX 24 GB.
What changes from the RTX 5060 Ti 16 GB to the RTX 3090 24 GB → · What changes from the RTX 5060 Ti 16 GB to the RTX 5070 Ti 16 GB → · What changes from the RTX 5060 Ti 16 GB to the RTX 4090 24 GB → · What changes from the RTX 5060 Ti 16 GB to the RTX 5090 32 GB →
05Questions
How much VRAM does SCAIL-2 need?
Around 12.6 GB with the smallest file (Q2_K) and 23.0 GB with an 8-bit file (FP8). The full 16-bit file needs about 38.1 GB.
Which SCAIL-2 file should I download for the RTX 5060 Ti 16 GB?
Q3_K_M (9.1 GB) from vantagewithai/SCAIL-2-GGUF-ComfyUI. Load it with Unet Loader (GGUF) from the ComfyUI-GGUF node pack.
FP8 or Q8_0 for SCAIL-2 on the RTX 5060 Ti 16 GB?
FP8 (17.7 GB): the RTX 5060 Ti 16 GB computes it natively, while Q8_0 (18.1 GB) is unpacked on the fly.
How much do I need to download for SCAIL-2 on the RTX 5060 Ti 16 GB?
17.4 GB: wan2.1_14B_SCAIL_2-Q3_K_M.gguf, umt5_xxl_fp8_e4m3fn_scaled.safetensors, Wan2_1_VAE_bf16.safetensors, clip_vision_h.safetensors.