What GPU do I need to run inclusionAI/Ming-Image-0.1-Design?
6.2B parameters, published in BF16. View on Hugging Face
Set up Ming-Image-0.1-DesignMing-Image-0.1-Design is published by inclusionAI on Hugging Face, with 0 downloads and 390 likes to date. It's a unlisted-architecture model built for text-to-image, published natively in BF16.
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) |
|---|---|---|---|---|---|
| BF16 | 11.5 GB | 13.8 GB | RTX 5060 Ti | 1 | $0.110/hr |
| FP8 (quantized) | 5.7 GB | 6.9 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 2.9 GB | 3.4 GB | RTX 5060 Ti | 1 | $0.110/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 Ming-Image-0.1-Design at its published (BF16) precision: 1× RTX 5060 Ti, at $0.110/hr per GPU ($0.110/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Ming-Image-0.1-Design: common questions
Does Ming-Image-0.1-Design fit on a 16 GB GPU?
Yes. At BF16 it needs 13.8 GB of VRAM, so a 16 GB card holds it with 2.2 GB to spare. A 12 GB card is not enough for it at BF16.
What is the least VRAM Ming-Image-0.1-Design can run in?
3.4 GB, at INT4 (quantized), which fits a 6 GB card, against 13.8 GB at BF16. 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.
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
