What GPU do I need to run Qwen/Qwen-Image-2.1?
7.1B parameters, published in BF16. View on Hugging Face
Set up Qwen-Image-2.1Qwen-Image-2.1 is published by Qwen on Hugging Face, with 102,510 downloads and 3,029 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 | 13.3 GB | 15.9 GB | RTX 5060 Ti | 1 | $0.110/hr |
| FP8 (quantized) | 6.6 GB | 8.0 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 3.3 GB | 4.0 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 Qwen-Image-2.1 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.
Qwen-Image-2.1: common questions
Does Qwen-Image-2.1 fit on a 16 GB GPU?
Yes. At BF16 it needs 15.9 GB of VRAM, so a 16 GB card holds it with 0.1 GB to spare. A 12 GB card is not enough for it at BF16.
What is the least VRAM Qwen-Image-2.1 can run in?
4.0 GB, at INT4 (quantized), which fits a 6 GB card, against 15.9 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.
More Qwen-Image models
- Qwen-Image-2.1-viggle-turbo (7.1B, BF16)
- Qwen-Image (20.4B, BF16)
- Qwen-Image-Edit-2511 (20.4B, BF16)
- Qwen-Image-2512 (20.4B, BF16)
- Qwen-Image-Bench (27.4B, BF16)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
