Can the Mac 128 GB run Ideogram 4?
Yes. Q8_0 fits on the Mac 128 GB with 92.8 GB to spare.
Q8_0 (10.1 GB (×2, both loaded)) needs about 22.6 GB in use, so 92.8 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 | ideogram4-transformer-q8_0.gguf main model Q8_0 · molbal/ideogram-4-gguf | models/unet | 10.1 GB | Download → |
| Model | ideogram4-unconditional_transformer-q8_0.gguf unconditional model, loaded together with the main one Q8_0 · molbal/ideogram-4-gguf | models/unet | 10.1 GB | Download → |
| Text encoder | qwen3vl_8b_fp8_scaled.safetensors Qwen3-VL 8B FP8 · Comfy-Org/Ideogram-4 | models/text_encoders | 10.6 GB | Download → |
| VAE | flux2-vae.safetensors FLUX.2 VAE · Comfy-Org/Ideogram-4 | models/vae | 0.3 GB | Download → |
| Total download · keep about the same free on disk | 31.2 GB | |||
On a Mac, FP8 text encoders do not load, so a 16-bit one is listed. Sizes from Hugging Face, 2026-10-01.
Shared memory: about 37.2 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 Ideogram 4 file on 115.4 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| Q8_0 ← GGUF · molbal | 10.1 GB ×2 | 22.6 GB | fits · 92.8 GB spare | practically identical to the original | Hugging Face → |
| Q5_1 GGUF · molbal | 7.3 GB ×2 | 17.0 GB | fits · 98.4 GB spare | close; small differences in fine detail | Hugging Face → |
| Q4_1 GGUF · molbal | 6.2 GB ×2 | 14.7 GB | fits · 100.7 GB spare | good; some loss in fine detail and text | Hugging Face → |
“Needed” adds 1.5 GB of working memory for Ideogram 4 and a 0.8 GB reserve. Sizes 2026-10-01. Needs two transformer files loaded together (main + unconditional). GGUF files need the molbal fork of ComfyUI-GGUF. No 16-bit weights were released. 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 Ideogram 4 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 Ideogram 4 need?
Around 14.7 GB with the smallest file (Q4_1) and 20.9 GB with an 8-bit file (FP8).
Which Ideogram 4 file should I download for the Mac 128 GB?
Q8_0 (10.1 GB) from molbal/ideogram-4-gguf. Load it with Unet Loader (GGUF) from the ComfyUI-GGUF node pack.
FP8 or Q8_0 for Ideogram 4 on the Mac 128 GB?
Q8_0 (10.1 GB). The Mac 128 GB has no FP8 maths, so FP8 (9.3 GB) only saves memory and is a step further from the original.
How much do I need to download for Ideogram 4 on the Mac 128 GB?
31.2 GB: ideogram4-transformer-q8_0.gguf, ideogram4-unconditional_transformer-q8_0.gguf, qwen3vl_8b_fp8_scaled.safetensors, flux2-vae.safetensors.