Can the Mac 16 GB run Wan 2.2 TI2V 5B?
Yes. Q8_0 fits on the Mac 16 GB with 3.5 GB to spare.
Take Q8_0 (5.4 GB). In use it comes to about 9.2 GB, with 3.5 GB left over on 12.7 GB.
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-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 | 12.9 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 18.9 GB of your 16 GB. On this machine the model, the text encoder, the VAE and the operating system all use the same memory. That is about 2.9 GB more than there is. It still runs: while the model works, the system compresses or swaps the idle text encoder, so loading and changing the prompt get slower. A smaller file or text encoder avoids that. calculated
02Every Wan 2.2 5B file on 12.7 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| 16-bit SAFETENSORS · Comfy-Org | 10.0 GB | 13.8 GB | spills 1.1 GB | the original weights | Hugging Face → |
| Q8_0 ← GGUF · QuantStack | 5.4 GB | 9.2 GB | fits · 3.5 GB spare | practically identical to the original | Hugging Face → |
| Q6_K GGUF · QuantStack | 4.2 GB | 8.0 GB | fits · 4.7 GB spare | very close to the original | Hugging Face → |
| Q5_K_M GGUF · QuantStack | 3.8 GB | 7.6 GB | fits · 5.1 GB spare | close; small differences in fine detail | Hugging Face → |
| Q4_K_M GGUF · QuantStack | 3.4 GB | 7.2 GB | fits · 5.5 GB spare | good; some loss in fine detail and text | Hugging Face → |
| Q3_K_M GGUF · QuantStack | 2.5 GB | 6.3 GB | fits · 6.4 GB spare | noticeable loss of detail | Hugging Face → |
| Q2_K GGUF · QuantStack | 1.9 GB | 5.7 GB | fits · 7.0 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 16 GB, send your time per image and peak VRAM and it will appear here, credited.
04About the Mac 16 GB
A Mac with 16 GB shares that memory between the CPU and the GPU. On current macOS the GPU may use about 12.7 GB of it by default (older macOS versions: about 11.5 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 16 GB: M1, M1 Pro, M2, M2 Pro, M3, M4, M5, M6 (memory bandwidth 68.25–200 GB/s - the higher, the faster). Everything about Macs and local AI →
16 GB unified · ~12.7 GB for the GPU · 68.25–200 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 16 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 Mac 16 GB?
12.9 GB: Wan2.2-TI2V-5B-Q8_0.gguf, umt5-xxl-encoder-Q8_0.gguf, wan2.2_vae.safetensors.