image model · qwen · macOS
Can I run Qwen-Image on Apple M3 Pro (18GB)?
Needs ~14 GB at Q4_K_M GGUF, but only ~12 GB is usable on Apple M3 Pro (18GB). With aggressive CPU offload it can run on as little as ~3 GB, much slower.
Needs ~14 GB at Q4_K_M GGUF, but only ~12 GB is usable on Apple M3 Pro (18GB). With aggressive CPU offload it can run on as little as ~3 GB, much slower.
The gap is about 2 GB: Qwen-Image needs roughly 14 GB and Apple M3 Pro (18GB) leaves only about 12 GB usable for a model. The lightest tracked hardware that runs Qwen-Image is the Nvidia GeForce RTX 4060 Ti (16GB) at 16 GB. See Qwen-Image on Nvidia GeForce RTX 4060 Ti (16GB).
- Peak VRAM
- ~14 GB
- Usable on device
- ~12 GB
- Device memory
- 18 GB
- Quant
- Q4_K_M GGUF
- Type
- image (MMDIT)
- Parameters
- 20B
- Peak VRAM
- ~14 GB at Q4_K_M GGUF
- Resolution
- 1328×1328
- License
- Apache-2.0
- Memory
- 18 GB unified
- Usable for weights
- ~12 GB
- Power draw
- ~70 W
- Best runtime
- Ollama (llama.cpp Metal backend) / MLX
What you can run instead
Run Qwen-Image on other hardware
FAQ
Can Apple M3 Pro (18GB) run Qwen-Image?
Needs ~14 GB at Q4_K_M GGUF, but only ~12 GB is usable on Apple M3 Pro (18GB). With aggressive CPU offload it can run on as little as ~3 GB, much slower.
How much VRAM does Qwen-Image need?
Apple M3 Pro (18GB) does not have enough memory. 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 2026-08-03. See methodology.