Can the Radeon 8060S (Strix Halo) 96 GB run Wan 2.2 Animate 14B?
Yes. 16-bit fits on the Radeon 8060S (Strix Halo) 96 GB with 56.2 GB to spare.
16-bit (34.5 GB) needs about 39.8 GB in use, so 56.2 GB of the 96 GB stays free: room for a larger image, a LoRA or a ControlNet.
01What to download
| Part | File | Folder | Size | |
|---|---|---|---|---|
| Model | wan2.2_animate_14B_bf16.safetensors 16-bit · Comfy-Org/Wan_2.2_ComfyUI_Repackaged | models/diffusion_models | 34.5 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_2.2_ComfyUI_Repackaged | 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 | 42.8 GB | |||
Alternatives: UMT5-XXL FP16 (11.4 GB). Sizes from Hugging Face, 2026-10-01.
Shared memory: about 48.8 GB of your 128 GB. On this machine the model, the text encoder, the VAE and the operating system all use the same memory. That leaves room, so nothing has to be swapped to disk. calculated
02Every Wan 2.2 Animate file on 96 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| 16-bit ← SAFETENSORS · Comfy-Org | 34.5 GB | 39.8 GB | fits · 56.2 GB spare | the original weights | Hugging Face → |
| Q8_0 GGUF · QuantStack | 18.7 GB | 24.0 GB | fits · 72.0 GB spare | practically identical to the original | Hugging Face → |
| FP8 SAFETENSORS · Kijai | 17.3 GB | 22.6 GB | fits · 73.4 GB spare | practically identical to the original | Hugging Face → |
| INT8 SAFETENSORS · Comfy-Org | 18.4 GB | 23.7 GB | fits · 72.3 GB spare | practically identical to the original | Hugging Face → |
| Q6_K GGUF · QuantStack | 14.6 GB | 19.9 GB | fits · 76.1 GB spare | very close to the original | Hugging Face → |
| Q5_K_M GGUF · QuantStack | 13.0 GB | 18.3 GB | fits · 77.7 GB spare | close; small differences in fine detail | Hugging Face → |
| Q4_K_M GGUF · QuantStack | 11.5 GB | 16.8 GB | fits · 79.2 GB spare | good; some loss in fine detail and text | Hugging Face → |
| Q3_K_M GGUF · QuantStack | 8.6 GB | 13.9 GB | fits · 82.1 GB spare | noticeable loss of detail | Hugging Face → |
| Q2_K GGUF · QuantStack | 6.5 GB | 11.8 GB | fits · 84.2 GB spare | heavy loss; a last resort | Hugging Face → |
“Needed” adds 4.5 GB of working memory for Wan 2.2 Animate 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 2.2 Animate on a Radeon 8060S (Strix Halo) 96 GB, send your time per image and peak VRAM and it will appear here, credited.
04About the Radeon 8060S (Strix Halo) 96 GB
This is an integrated GPU that uses system memory. AMD states that up to 96 GB of the 128 GB can be set aside as graphics memory on Windows, which is why it can hold models no consumer graphics card can. The catch is speed: the GPU itself (40 compute units) is far smaller than a big desktop card, and its memory bandwidth is a fraction of one, so each step takes much longer. Treat the verdicts here as 'it fits', not 'it is fast'.
96 GB LPDDR5X (shared system memory) · 256-bit · 256 GB/s · RDNA 3.5. Everything that runs on it →
05Questions
How much VRAM does Wan 2.2 Animate need?
Around 11.8 GB with the smallest file (Q2_K) and 22.6 GB with an 8-bit file (FP8). The full 16-bit file needs about 39.8 GB.
Which Wan 2.2 Animate file should I download for the Radeon 8060S (Strix Halo) 96 GB?
16-bit (34.5 GB) from Comfy-Org/Wan_2.2_ComfyUI_Repackaged.
FP8 or Q8_0 for Wan 2.2 Animate on the Radeon 8060S (Strix Halo) 96 GB?
Q8_0 (18.7 GB). The Radeon 8060S (Strix Halo) 96 GB has no FP8 maths, so FP8 (17.3 GB) only saves memory and is a step further from the original.
How much do I need to download for Wan 2.2 Animate on the Radeon 8060S (Strix Halo) 96 GB?
42.8 GB: wan2.2_animate_14B_bf16.safetensors, umt5_xxl_fp8_e4m3fn_scaled.safetensors, wan_2.1_vae.safetensors, clip_vision_h.safetensors.