Can the Mac 128 GB run Wan 2.2 TI2V 5B?
Yes. 16-bit fits on the Mac 128 GB with 101.6 GB to spare.
16-bit (10.0 GB) needs about 13.8 GB in use, so 101.6 GB of the 115.4 GB stays free: room for a larger image, a LoRA or a ControlNet.
01What to download
| Part | File | Folder | Size | |
|---|---|---|---|---|
| Model | wan2.2_ti2v_5B_fp16.safetensors 16-bit · Comfy-Org/Wan_2.2_ComfyUI_Repackaged | models/diffusion_models | 10.0 GB | Download → |
| Text encoder | umt5-xxl-encoder-Q8_0.gguf UMT5-XXL GGUF Q8_0 · city96/umt5-xxl-encoder-gguf | models/text_encoders | 6.0 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 | 17.5 GB | |||
Alternatives: UMT5-XXL FP16 (11.4 GB). On a Mac, FP8 text encoders do not load, so a 16-bit one is listed - or a GGUF one, loaded with “CLIPLoader (GGUF)” from the ComfyUI-GGUF nodes. 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 →
Shared memory: about 23.5 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 5B file on 115.4 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| 16-bit ← SAFETENSORS · Comfy-Org | 10.0 GB | 13.8 GB | fits · 101.6 GB spare | the original weights | Hugging Face → |
| Q8_0 GGUF · QuantStack | 5.4 GB | 9.2 GB | fits · 106.2 GB spare | practically identical to the original | Hugging Face → |
| Q6_K GGUF · QuantStack | 4.2 GB | 8.0 GB | fits · 107.4 GB spare | very close to the original | Hugging Face → |
| Q5_K_M GGUF · QuantStack | 3.8 GB | 7.6 GB | fits · 107.8 GB spare | close; small differences in fine detail | Hugging Face → |
| Q4_K_M GGUF · QuantStack | 3.4 GB | 7.2 GB | fits · 108.2 GB spare | good; some loss in fine detail and text | Hugging Face → |
| Q3_K_M GGUF · QuantStack | 2.5 GB | 6.3 GB | fits · 109.1 GB spare | noticeable loss of detail | Hugging Face → |
| Q2_K GGUF · QuantStack | 1.9 GB | 5.7 GB | fits · 109.7 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
Nobody has sent measured numbers for this pair yet. If you run Wan 2.2 5B on a Mac 128 GB, send your time per image and peak VRAM and it will appear here, credited.
04About the Mac 128 GB
A Mac with 128 GB shares that memory between the CPU and the GPU. On current macOS the GPU may use about 115.4 GB of it by default (older macOS versions: about 103.1 GB); that is the figure used here. ComfyUI can go past it, but macOS then starts compressing and swapping memory and everything slows down. ComfyUI runs on Apple GPUs through PyTorch's MPS backend. 16-bit and GGUF files work; FP8 and INT8 files do not save memory on a Mac, so they are skipped. Speed is the catch: even the fastest Macs are several times slower per image than a desktop RTX card. Chips sold with 128 GB: M1 Ultra, M2 Ultra, M3 Max, M4 Max, M5 Max (memory bandwidth 300–800 GB/s - the higher, the faster). Everything about Macs and local AI →
128 GB unified · ~115.4 GB for the GPU · 300–800 GB/s · Apple Silicon. 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 Mac 128 GB?
16-bit (10.0 GB) from Comfy-Org/Wan_2.2_ComfyUI_Repackaged.
How much do I need to download for Wan 2.2 5B on the Mac 128 GB?
17.5 GB: wan2.2_ti2v_5B_fp16.safetensors, umt5-xxl-encoder-Q8_0.gguf, wan2.2_vae.safetensors.