Can the Mac 32 GB run Wan 2.2 S2V 14B?
Yes, but only just: Q8_0 leaves 2.4 GB free on the Mac 32 GB.
Take Q8_0 (19.6 GB). In use it comes to about 24.4 GB, with 2.4 GB left over on 26.8 GB.
01What to download
| Part | File | Folder | Size | |
|---|---|---|---|---|
| Model | Wan2.2-S2V-14B-Q8_0.gguf Q8_0 · QuantStack/Wan2.2-S2V-14B-GGUF | models/unet | 19.6 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 | wan_2.1_vae.safetensors Wan 2.1 VAE · Comfy-Org/Wan_2.2_ComfyUI_Repackaged | models/vae | 0.3 GB | Download → |
| Also needed | wav2vec2_large_english_fp16.safetensors wav2vec2 large English FP16 (audio encoder) · Comfy-Org/Wan_2.2_ComfyUI_Repackaged | models/audio_encoders | 0.6 GB | Download → |
| Total download · keep about the same free on disk | 26.5 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.
Shared memory: about 32.5 GB of your 32 GB. On this machine the model, the text encoder, the VAE and the operating system all use the same memory. That is about 0.5 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 S2V file on 26.8 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| 16-bit SAFETENSORS · Comfy-Org | 32.6 GB | 37.4 GB | 10.6 GB too big | the original weights | Hugging Face → |
| Q8_0 ← GGUF · QuantStack | 19.6 GB | 24.4 GB | fits · 2.4 GB spare | practically identical to the original | Hugging Face → |
| Q6_K GGUF · QuantStack | 16.2 GB | 21.0 GB | fits · 5.8 GB spare | very close to the original | Hugging Face → |
| Q5_K_M GGUF · QuantStack | 15.0 GB | 19.8 GB | fits · 7.0 GB spare | close; small differences in fine detail | Hugging Face → |
| Q4_K_M GGUF · QuantStack | 13.9 GB | 18.7 GB | fits · 8.1 GB spare | good; some loss in fine detail and text | Hugging Face → |
| Q3_K_M GGUF · QuantStack | 11.4 GB | 16.2 GB | fits · 10.6 GB spare | noticeable loss of detail | Hugging Face → |
| Q2_K GGUF · QuantStack | 9.5 GB | 14.3 GB | fits · 12.5 GB spare | heavy loss; a last resort | Hugging Face → |
“Needed” adds 4 GB of working memory for Wan 2.2 S2V and a 0.8 GB reserve. Sizes 2026-10-01. FP8 and INT8 files are left out on a Mac: Apple GPUs cannot compute FP8, so ComfyUI either fails to load them or converts them back to 16-bit, which saves no memory. Use a 16-bit or GGUF file.
03Measured and reported results
Nobody has sent measured numbers for this pair yet. If you run Wan 2.2 S2V on a Mac 32 GB, send your time per image and peak VRAM and it will appear here, credited.
04About the Mac 32 GB
A Mac with 32 GB shares that memory between the CPU and the GPU. On current macOS the GPU may use about 26.8 GB of it by default (older macOS versions: about 22.9 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 32 GB: M1 Pro, M1 Max, M2 Pro, M2 Max, M4, M5, M6 (memory bandwidth 120–400 GB/s - the higher, the faster). Everything about Macs and local AI →
32 GB unified · ~26.8 GB for the GPU · 120–400 GB/s · Apple Silicon. Everything that runs on it →
05Questions
How much VRAM does Wan 2.2 S2V need?
Around 14.3 GB with the smallest file (Q2_K) and 21.2 GB with an 8-bit file (FP8). The full 16-bit file needs about 37.4 GB.
Which Wan 2.2 S2V file should I download for the Mac 32 GB?
Q8_0 (19.6 GB) from QuantStack/Wan2.2-S2V-14B-GGUF. Load it with Unet Loader (GGUF) from the ComfyUI-GGUF node pack.
FP8 or Q8_0 for Wan 2.2 S2V on the Mac 32 GB?
Q8_0 (19.6 GB). The Mac 32 GB has no FP8 maths, so FP8 (16.4 GB) only saves memory and is a step further from the original.
How much do I need to download for Wan 2.2 S2V on the Mac 32 GB?
26.5 GB: Wan2.2-S2V-14B-Q8_0.gguf, umt5-xxl-encoder-Q8_0.gguf, wan_2.1_vae.safetensors, wav2vec2_large_english_fp16.safetensors.