Can the RTX 3080 10 GB run Ideogram 4?
Not entirely. Even Q4_1 is 4.7 GB over 10 GB, so ComfyUI keeps part of it in system RAM.
The smallest sensible file, Q4_1 (6.2 GB (×2, both loaded)), needs about 14.7 GB - 4.7 GB more than RTX 3080 10 GB has. ComfyUI can still run it by streaming part of the model from system RAM. How much slower that is depends on your ComfyUI version, the file format and the PCIe link - see the note below.
Also try the FP8 file (9.3 GB) on this GPU. It does not fit entirely, but a current ComfyUI streams the overflow from RAM and native 8-bit files often beat a small GGUF that does fit. Dynamic VRAM →
01With more VRAM
With 24 GB you could run Ideogram 4 at 8-bit or better (Q8_0, 10.1 GB) with no offloading: for example on the RTX 4090 24 GB, RTX 3090 24 GB, RX 7900 XTX 24 GB.
What changes from the RTX 3080 10 GB to the RTX 3090 24 GB → · What changes from the RTX 3080 10 GB to the RTX 5070 Ti 16 GB →
02Every Ideogram 4 file on 10 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| Q8_0 GGUF · molbal | 10.1 GB ×2 | 22.6 GB | 12.6 GB too big | practically identical to the original | Hugging Face → |
| FP8 SAFETENSORS · Comfy-Org | 9.3 GB ×2 | 20.9 GB | 10.9 GB too big | practically identical to the original | Hugging Face → |
| INT8 SAFETENSORS · Comfy-Org | 9.6 GB ×2 | 21.5 GB | 11.5 GB too big | practically identical to the original | Hugging Face → |
| Q5_1 GGUF · molbal | 7.3 GB ×2 | 17.0 GB | 7.0 GB too big | close; small differences in fine detail | Hugging Face → |
| Q4_1 ← GGUF · molbal | 6.2 GB ×2 | 14.7 GB | 4.7 GB too big | 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.
03What to download
| Part | File | Folder | Size | |
|---|---|---|---|---|
| Model | ideogram4-transformer-q4_1.gguf main model Q4_1 · molbal/ideogram-4-gguf | models/unet | 6.2 GB | Download → |
| Model | ideogram4-unconditional_transformer-q4_1.gguf unconditional model, loaded together with the main one Q4_1 · molbal/ideogram-4-gguf | models/unet | 6.2 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 | 23.3 GB | |||
Alternatives: Qwen3-VL 8B NVFP4 (6.3 GB). FP4 files are listed only as alternatives unless the GPU is an RTX 50 (Blackwell). Sizes from Hugging Face, 2026-10-01.
04The text encoder
Take the smallest Qwen3-VL 8B file (6.3 GB): it is the only one that fits in 10 GB on its own. Sizes: FP8 10.6 GB, NVFP4 6.3 GB. Why the encoder does not count against VRAM →
05Measured and reported results
Nobody has sent measured numbers for this pair yet. If you run Ideogram 4 on a RTX 3080 10 GB, send your time per image and peak VRAM and it will appear here, credited.
06About the RTX 3080 10 GB
The RTX 3080 10 GB (Ampere) has no FP8 compute. FP8 files still load and save memory, but ComfyUI converts them back before the maths, so there is no speed gain - Q8_0 GGUF is the closer-to-original 8-bit choice. ComfyUI can compute INT8 files natively on NVIDIA, which is worth a try where a model offers one.
10 GB GDDR6X · 320-bit · 760 GB/s · Ampere. Everything that runs on it →
07Questions
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 RTX 3080 10 GB?
Q4_1 (6.2 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 RTX 3080 10 GB?
Q8_0 (10.1 GB). The RTX 3080 10 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 RTX 3080 10 GB?
23.3 GB: ideogram4-transformer-q4_1.gguf, ideogram4-unconditional_transformer-q4_1.gguf, qwen3vl_8b_fp8_scaled.safetensors, flux2-vae.safetensors.