What GPU do I need to run lightx2v/Qwen-Image-2.1-Lightning-v2-RL?
7.1B parameters, published in F32. View on Hugging Face
Set up Qwen-Image-…-Lightning-v2-RLQwen-Image-2.1-Lightning-v2-RL is published by lightx2v on Hugging Face, with 10,514 downloads and 41 likes to date. It's a unlisted-architecture model built for text-to-image, published natively in F32.
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) |
|---|---|---|---|---|---|
| FP32 | 26.5 GB | 31.8 GB | V100 | 1 | $0.220/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-Lightning-v2-RL at its published (F32) precision: 1× V100, at $0.220/hr per GPU ($0.220/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-Lightning-v2-RL: common questions
Does Qwen-Image-2.1-Lightning-v2-RL fit on a 32 GB GPU?
Yes. At FP32 it needs 31.8 GB of VRAM, so a 32 GB card holds it with 0.2 GB to spare. A 24 GB card is not enough for it at FP32.
Can Qwen-Image-2.1-Lightning-v2-RL run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 26.5 GB, or 31.8 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 13.3 GB, or 15.9 GB with overhead. That moves it onto a 16 GB card instead of a 32 GB one. How much accuracy the cast costs is model-specific and is not measured here.
What is the least VRAM Qwen-Image-2.1-Lightning-v2-RL can run in?
4.0 GB, at INT4 (quantized), which fits a 6 GB card, against 31.8 GB at FP32. 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-Lightning-v2-RL lower the GPU bill?
Yes. At FP32 the cheapest live fit is one V100 at $0.220/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-TailSteer (7.1B, BF16)
- Qwen-Image (20.4B, BF16)
Lineage
Based on Qwen-Image-2.1.
Alternatives at this size
Other models for text-to-image within about a third of Qwen-Image-2.1-Lightning-v2-RL's 7.1B parameters, from other model lines.
- Z-Image-Turbo (6.2B, F32)
- stable-diffusion-3.5-large (8.1B, BF16)
- Chroma1-HD (8.9B, BF16)
- FIBO (8.3B, BF16)
- Juggernaut-Z-Image (6.2B, F16)
More on Qwen-Image-2.1-Lightning-v2-RL
Fits on a 32 GB GPU at FP32: every model that fits in 32 GB.
Fits on an 8 GB GPU at FP8: every model that fits in 8 GB.
Related reading: V100 pricing and specs, Fast diffusion inference on GPU VMs, What AI inference is, and What AI model fine-tuning is.
