What GPU do I need to run Qwen/Qwen-Image-Bench?
27.4B parameters, published in BF16. View on Hugging Face
Set up Qwen-Image-BenchQwen-Image-Bench is published by Qwen on Hugging Face, with 42,579 downloads and 93 likes to date. It's a Qwen3_5ForConditionalGeneration model built for image-text-to-text, 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 | 51.0 GB | 61.1 GB | A100 | 1 | $1.21/hr |
| cheaper alt. | RTX A5000 | 3 | $0.528/hr | ||
| FP8 (quantized) | 25.5 GB | 30.6 GB | RTX 5000 Ada | 1 | $0.537/hr |
| cheaper alt. | RTX 4080 | 2 | $0.347/hr | ||
| INT4 (quantized) | 12.7 GB | 15.3 GB | RTX A4000 | 1 | $0.167/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-Bench at its published (BF16) precision: 1× A100, at $1.21/hr per GPU ($1.21/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-Bench: 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.
Hybrid: only the full-attention layers cache per token; the other layers keep a fixed-size state. Cached per token: 65,536 bytes at 16-bit.
| Context | KV cache, one sequence |
|---|---|
| 4K | 0.25 GB |
| 32K | 2.0 GB |
| 128K | 8.0 GB |
| 256K (model maximum) | 16.0 GB |
Computed from the layer, head and window counts in the model's own configuration (Qwen/Qwen-Image-Bench), at 16 bits per cached value. Each concurrent sequence needs its own cache, on top of the weights.
Qwen-Image-Bench: common questions
Can Qwen-Image-Bench run on a single GPU?
Yes, but not on a desktop card. At BF16 it needs 61.1 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 80.0 GB A100 at $1.21/hr.
What is the least VRAM Qwen-Image-Bench can run in?
15.3 GB, at INT4 (quantized), which fits a 16 GB card, against 61.1 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-Bench lower the GPU bill?
Yes. At BF16 the cheapest live fit is one A100 at $1.21/hr. At INT4 (quantized) it drops to one RTX A4000 at $0.167/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 (20.4B, BF16)
- Qwen-Image-Edit-2511 (20.4B, BF16)
- Qwen-Image-2512 (20.4B, BF16)
- Qwen-Image-2.1-viggle-turbo (7.1B, BF16)
- Qwen-Image-2.1 (7.1B, BF16)
Lineage
Based on Qwen3.6-27B.
Alternatives at this size
Other models for image-text-to-text within about a third of Qwen-Image-Bench's 27.4B parameters, from other model lines.
- gemma-4-31B-it (31.3B, BF16)
- diffusiongemma-26B-A4B-it (25.8B, BF16)
- Muse-Glimmer-30B (29.8B, BF16)
- Infinity-Parser2-Pro (35.1B, BF16)
- InternVL2-26B (25.5B, BF16)
More on Qwen-Image-Bench
Fits on an 80 GB GPU at BF16: every model that fits in 80 GB.
Fits on a 32 GB GPU at FP8: every model that fits in 32 GB.
Fits on a 16 GB GPU at INT4: every model that fits in 16 GB.
Best vision-language models: how Qwen-Image-Bench ranks against the rest.
Related reading: A100 pricing and specs, How much VRAM you need for LLMs, Serving LLMs with vLLM, vLLM vs TensorRT-LLM vs SGLang, and Best GPU for LLM inference.
