Can the Mac 24 GB run Wan 2.2 I2V A14B?
Download Q6_K (12.0 GB). With the model's working memory it needs about 16.3 GB, leaving 3.3 GB spare on 19.6 GB. Quality: very close to the original. For better quality, Q8_0 (15.4 GB) also runs, with about 0.1 GB spilling into system RAM — a little slower, still practical.
01What to download
| Part | File | Folder | Size | |
|---|---|---|---|---|
| Model | Wan2.2-I2V-A14B-HighNoise-Q6_K.gguf high-noise model (early steps) Q6_K · QuantStack/Wan2.2-I2V-A14B-GGUF | models/unet | 12.0 GB | Download → |
| Model | Wan2.2-I2V-A14B-LowNoise-Q6_K.gguf low-noise model (late steps) Q6_K · QuantStack/Wan2.2-I2V-A14B-GGUF | models/unet | 12.0 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 | wan_2.1_vae.safetensors Wan 2.1 VAE · Comfy-Org/Wan_2.2_ComfyUI_Repackaged | models/vae | 0.3 GB | Download → |
| Total download · keep about the same free on disk | 30.3 GB | |||
Alternatives: UMT5-XXL FP16 (11.4 GB). The 16-bit text encoder is a little more faithful; the smaller one is chosen here because it loads faster and needs less RAM. 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 read from Hugging Face (2026-09-25). “Download” links start the file directly; the file name links are the same files the official ComfyUI workflows use.
Shared memory: about 36.3 GB of your 24 GB. On this machine the model, the text encoder, the VAE and the operating system all use the same memory. That is about 12.3 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 I2V file on 19.6 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| 16-bit SAFETENSORS · Comfy-Org | 28.6 GB | 32.9 GB | 13.3 GB too big | the original weights | Hugging Face → |
| Q8_0 GGUF · QuantStack | 15.4 GB | 19.7 GB | spills 0.1 GB | practically identical to the original | Hugging Face → |
| Q6_K ← GGUF · QuantStack | 12.0 GB | 16.3 GB | fits · 3.3 GB spare | very close to the original | Hugging Face → |
| Q5_K_M GGUF · QuantStack | 10.8 GB | 15.1 GB | fits · 4.5 GB spare | close; small differences in fine detail | Hugging Face → |
| Q4_K_M GGUF · QuantStack | 9.7 GB | 14.0 GB | fits · 5.6 GB spare | good; some loss in fine detail and text | Hugging Face → |
| Q3_K_M GGUF · QuantStack | 7.2 GB | 11.5 GB | fits · 8.1 GB spare | noticeable loss of detail | Hugging Face → |
| Q2_K GGUF · QuantStack | 5.3 GB | 9.6 GB | fits · 10.0 GB spare | heavy loss; a last resort | Hugging Face → |
Sizes from the Hugging Face file listing, read 2026-09-25. “Needed” = file + 3.5 GB working memory for this model + 0.8 GB kept free for the system. Wan 2.2 A14B uses two files of this size (high-noise and low-noise); only one sits in VRAM at a time, both must fit in system RAM. 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.
03The text encoder
UMT5-XXL + CLIP Vision H: 11.4 GB as 16-bit, 6.7 GB as FP8, 3.7 GB as GGUF Q4_K_M (plus CLIP Vision H (1.3 GB) for the input image). ComfyUI encodes the prompt first and can push the encoder out of VRAM before sampling, so it does not have to fit together with the model. On 19.6 GB the FP8 encoder fits on its own, so prompt encoding stays fast.
04About this GPU
A Mac with 24 GB shares that memory between the CPU and the GPU. On current macOS the GPU may use about 19.6 GB of it by default (older macOS versions: about 17.2 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 24 GB: M2, M3, M4, M4 Pro, M5, M5 Pro, M6 (memory bandwidth 100–307 GB/s — the higher, the faster). Everything about Macs and local AI →
Mac 24 GB: 24 GB unified · ~19.6 GB for the GPU · 100–307 GB/s · Apple Silicon. Everything that runs on the Mac 24 GB →
05What more VRAM would change
With 20 GB you could run Wan 2.2 I2V at 8-bit or better (Q8_0, 15.4 GB) with no offloading.
06Measured and reported results
Nobody has sent measured numbers for this pair yet. If you run Wan 2.2 I2V on a Mac 24 GB, send your time per image and peak VRAM and it will appear here, credited.
07Questions
How much VRAM does Wan 2.2 I2V need?
Around 9.6 GB with the smallest file (Q2_K) and 18.6 GB with an 8-bit file (FP8), counting working memory and a small system reserve. The full 16-bit file needs about 32.9 GB.
Which Wan 2.2 I2V file should I download for the Mac 24 GB?
Q6_K (12.0 GB) from QuantStack/Wan2.2-I2V-A14B-GGUF. Load it with Unet Loader (GGUF) from the ComfyUI-GGUF node pack.
Is FP8 faster than GGUF on the Mac 24 GB?
No. This GPU has no FP8 compute, so ComfyUI converts FP8 weights back before the maths. FP8 only saves memory here; Q8_0 GGUF is the closer-to-original 8-bit pick.
How much do I need to download for Wan 2.2 I2V on the Mac 24 GB?
About 30.3 GB for the model file, text encoder and VAE listed on this page, and the same again free on disk. With a 19.6 GB card, 48 GB of system RAM or more is recommended.