What GPU do I need to run ideogram-ai/ideogram-4-fp8?
9.3B parameters, published in F8_E4M3. View on Hugging FaceGated
Set up ideogram-4-fp8ideogram-4-fp8 is published by ideogram-ai on Hugging Face, with 54,111 downloads and 774 likes to date. It's a unlisted-architecture model built for text-to-image, published natively in F8_E4M3, and gated: you'll need to accept the model's terms on Hugging Face before downloading weights.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for activation memory and allocator fragmentation. Diffusion and video models carry no KV-cache. The real driver of extra memory is output resolution and frame count, which this flat overhead does not model. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP8 (native) | 8.6 GB | 10.4 GB | RTX 4070 | 1 | $0.121/hr |
| INT4 (quantized) | 4.3 GB | 5.2 GB | RTX 3070 | 1 | $0.088/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run ideogram-4-fp8 at its published (F8_E4M3) precision: 1× RTX 4070, at $0.121/hr per GPU ($0.121/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
ideogram-4-fp8: common questions
Does ideogram-4-fp8 fit on a 12 GB GPU?
Yes. At FP8 (native) it needs 10.4 GB of VRAM, so a 12 GB card holds it with 1.6 GB to spare. An 8 GB card is not enough for it at FP8 (native).
Do I need approval to download ideogram-4-fp8?
Yes. ideogram-ai gates this repository on Hugging Face, so you have to accept its terms with a signed-in account before the weights will download. It does not change the 10.4 GB the model needs once you have them.
Is ideogram-4-fp8 already quantized?
Yes. It is published in FP8, one byte per parameter, so the 10.4 GB figure is already a quantized footprint rather than a full-precision one. Only INT4 goes below it, at 5.2 GB. A GPU without FP8 tensor cores cannot run it as published, which is why cards here are matched on precision support and not on VRAM alone.
What is the least VRAM ideogram-4-fp8 can run in?
5.2 GB, at INT4 (quantized), which fits a 6 GB card, against 10.4 GB at FP8 (native). That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More ideogram-ai models
- bert-base-uncased (110M, F32)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
- Qwen3-8B (8.2B, BF16)
- Qwen3.6-35B-A3B-FP8 (36.0B, F8_E4M3)
- Qwen3.5-9B (9.7B, BF16)
Related reading: RTX 4070 pricing and specs, The NVIDIA Inception program, explained, Google Colab alternatives for dedicated GPU access, Free GPU credits for students and researchers, and RunPod volume disk vs. network volume, compared.
