What GPU do I need to run Lyricccco/Qwen-Image-2.1-TailSteer?
7.1B parameters, published in BF16. View on Hugging Face
Set up Qwen-Image-2.1-TailSteerQwen-Image-2.1-TailSteer is published by Lyricccco on Hugging Face, with 122 downloads and 38 likes to date. It's a unlisted-architecture model built for any-to-any, published natively in BF16.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision times a flat 1.2 overhead for activations and fragmentation. The KV-cache grows with context and is not in that factor; it is listed per model below where the architecture is published. 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 3090 | 1 | $0.162/hr |
| FP8 (quantized) | 6.6 GB | 8.0 GB | RTX 4070 | 1 | $0.154/hr |
| INT4 (quantized) | 3.3 GB | 4.0 GB | RTX 3060 | 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-TailSteer at its published (BF16) precision: 1× RTX 3090, at $0.162/hr per GPU ($0.162/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-TailSteer: KV cache by context length
The KV cache is the memory the attention layers hold for every token of context, on top of the weights. It is not part of the flat 1.2x overhead in the table above, grows with context length and with every concurrent request, and is why a long-context deployment needs more VRAM than the table shows.
KV-cache size for Qwen-Image-2.1-TailSteer is not published for this architecture, so no figure is shown.
Qwen-Image-2.1-TailSteer: common questions
Does Qwen-Image-2.1-TailSteer 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-TailSteer 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.
Does quantizing Qwen-Image-2.1-TailSteer lower the GPU bill?
Yes. At BF16 the cheapest live fit is one RTX 3090 at $0.162/hr. At INT4 (quantized) it drops to one RTX 3060 at $0.110/hr, provided a quantized checkpoint exists for 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-2.1 (7.1B, BF16)
- Qwen-Image-2.1-Turbo (7.1B, BF16)
- Qwen-Image-2.1-Lightning-v2-RL (7.1B, F32)
- Qwen-Image (20.4B, BF16)
Lineage
Based on Qwen-Image-2.1.
Alternatives at this size
Other models for any-to-any within about a third of Qwen-Image-2.1-TailSteer's 7.1B parameters, from other model lines.
- gemma-4-E4B-it (8.0B, BF16)
More on Qwen-Image-2.1-TailSteer
Fits on a 16 GB GPU at BF16: every model that fits in 16 GB.
Fits on an 8 GB GPU at FP8: every model that fits in 8 GB.
Related reading: RTX 3090 pricing and specs, How much VRAM you need for LLMs, Serving LLMs with vLLM, vLLM vs TensorRT-LLM vs SGLang, Best GPU for LLM inference, and What AI model fine-tuning is.
