Can the RTX 5070 Laptop 8 GB run Ideogram 4?
Not entirely. Even Q4_1 is 6.7 GB over 8 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 - 6.7 GB more than RTX 5070 Laptop 8 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 (FP8, 9.3 GB) with no offloading: for example on the RTX 4090 24 GB, RTX 3090 24 GB, RX 7900 XTX 24 GB.
02Every Ideogram 4 file on 8 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| FP8 SAFETENSORS · Comfy-Org | 9.3 GB ×2 | 20.9 GB | 12.9 GB too big | practically identical to the original | Hugging Face → |
| INT8 SAFETENSORS · Comfy-Org | 9.6 GB ×2 | 21.5 GB | 13.5 GB too big | practically identical to the original | Hugging Face → |
| Q8_0 GGUF · molbal | 10.1 GB ×2 | 22.6 GB | 14.6 GB too big | practically identical to the original | Hugging Face → |
| Q5_1 GGUF · molbal | 7.3 GB ×2 | 17.0 GB | 9.0 GB too big | close; small differences in fine detail | Hugging Face → |
| Q4_1 ← GGUF · molbal | 6.2 GB ×2 | 14.7 GB | 6.7 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 8 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 5070 Laptop 8 GB, send your time per image and peak VRAM and it will appear here, credited.
06About the RTX 5070 Laptop 8 GB
The RTX 5070 Laptop 8 GB is a Blackwell GPU with FP8 and FP4 hardware: ComfyUI computes Comfy-Org's FP8 files natively here, and NVFP4 files (where a model offers them) are faster still. As a laptop GPU it runs at a lower power limit than desktop cards (50–100 W depending on the laptop). The memory verdicts are the same; speed depends heavily on how much power the laptop maker allows.
8 GB GDDR7 · 128-bit · 384 GB/s · Blackwell · 50–100 W. 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 5070 Laptop 8 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 5070 Laptop 8 GB?
FP8 (9.3 GB): the RTX 5070 Laptop 8 GB computes it natively, while Q8_0 (10.1 GB) is unpacked on the fly.
How much do I need to download for Ideogram 4 on the RTX 5070 Laptop 8 GB?
23.3 GB: ideogram4-transformer-q4_1.gguf, ideogram4-unconditional_transformer-q4_1.gguf, qwen3vl_8b_fp8_scaled.safetensors, flux2-vae.safetensors.