image model · qwen · Windows
Can I run Qwen-Image on Nvidia GeForce RTX 4080 (16GB)?
Yes. Qwen-Image runs on Nvidia GeForce RTX 4080 (16GB) at Q4_K_M GGUF (~14 GB of ~15 GB usable).
Fits at Q4_K_M GGUF (~14 GB of ~15 GB usable) but with little headroom; close other apps.
- Peak VRAM
- ~14 GB
- Usable on device
- ~15 GB
- Device memory
- 16 GB
- Quant
- Q4_K_M GGUF
How to run it
Use ComfyUI or Nunchaku (SVDQuant 4-bit) at Q4_K_M GGUF. The big text encoder is loaded to encode your prompt, then offloaded before generation, which is why peak VRAM stays near the backbone size rather than the sum of every file.
- Type
- image (MMDIT)
- Parameters
- 20B
- Peak VRAM
- ~14 GB at Q4_K_M GGUF
- Resolution
- 1328×1328
- License
- Apache-2.0
- Memory
- 16 GB vram
- Usable for weights
- ~15 GB
- Best runtime
- vLLM (Linux) / Ollama (CUDA)
You could also run
Run Qwen-Image on other hardware
FAQ
Can Nvidia GeForce RTX 4080 (16GB) run Qwen-Image?
Yes. Qwen-Image runs on Nvidia GeForce RTX 4080 (16GB) at Q4_K_M GGUF (~14 GB of ~15 GB usable).
How much VRAM does Qwen-Image need?
It is a tight fit on Nvidia GeForce RTX 4080 (16GB). At Q4_K_M GGUF the realistic peak is ~14 GB of VRAM, versus ~57 GB with every component kept resident (no offload). With aggressive CPU offload it drops to ~3 GB, much slower.
What do I use to run Qwen-Image locally?
Qwen-Image runs in ComfyUI or Nunchaku (SVDQuant 4-bit). It loads as a diffusion checkpoint plus its text encoder and VAE, not a single chat command.
Sources
VRAM figures are sourced peak-usage anchors at the noted quant, validated 2026-06-15. See methodology.