Image models · Alibaba

Qwen-Image 2.1 Turbo

Name: Qwen-Image 2.1 Turbo
Author: Alibaba

Backbone: 7.1B
Native output: 2048×2048
Default steps: 8
License: qwen-research
Released: 2026-10

How to run it

Quantized files · GGUF for ComfyUI

Smaller GGUF versions for ComfyUI (with the ComfyUI-GGUF node). No sign-in needed; the model’s license still applies.

huggingface.co/unsloth/Qwen-Image-2.1-Turbo-GGUF (opens in a new tab)

Original weights · Hugging Face

Full-precision files from Alibaba, for fine-tuning, vLLM, Transformers, Diffusers or ComfyUI.

huggingface.co/Qwen/Qwen-Image-2.1-Turbo (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Alibaba. The weights themselves download from Hugging Face.

github.com/QwenLM/Qwen-Image-2.1 (opens in a new tab)

git clone https://github.com/QwenLM/Qwen-Image-2.1

Links checked 1 day ago against Hugging Face, Ollama and GitHub.

Apps that run Qwen-Image 2.1 Turbo

Our pick for each kind of computer, assuming the model fits in its memory. Pick your own GPU or device in the Model Finder for an exact answer.

Mac (Apple silicon)

Draw Things

Native Mac app with its own fast Metal engine and quantized downloads.

Also good:

Windows PC, NVIDIA GPU

ComfyUI

Runs this model with built-in or popular custom nodes; GGUF files load with ComfyUI-GGUF.

Also good:

Linux, NVIDIA or AMD GPU

ComfyUI

Runs this model with built-in or popular custom nodes; GGUF files load with ComfyUI-GGUF.

Also good:

App	Type	Opens it as	Systems	Engine	Start
Unsloth Desktop	Desktop app	GGUF	macOSWindowsLinux	llama.cpp, MLX, stable-diffusion.cpp	Open in Unsloth Desktop
ComfyUI	Desktop app	Original or GGUF	macOSWindowsLinux	PyTorch, GGUF via ComfyUI-GGUF	Get ComfyUI
Draw Things	Desktop app	Built-in support	macOSiOS	Draw Things	Open in Draw Things
SwarmUI	Desktop app	Original or GGUF	macOSWindowsLinux	ComfyUI	Get SwarmUI
Forge Neo	Desktop app	Built-in support	WindowsLinux	PyTorch	Get Forge Neo
SD.Next	Desktop app	Original or GGUF	macOSWindowsLinux	PyTorch	Get SD.Next
Stability Matrix	Desktop app	Original or GGUF	macOSWindowsLinux	installs ComfyUI, Forge, InvokeAI, SwarmUI, SD.Next or Fooocus	Get Stability Matrix

Which apps can open a model depends on the files published for it: GGUF for llama.cpp-based apps, MLX for Apple’s engine, the original weights for vLLM and SGLang. Compare every app.

Memory needed

Peak GPU memory at the native output size. Offloading moves the text encoder, or the whole pipeline, to system RAM between steps.

Precision	Everything on GPU	Text encoder offloaded	Sequential offload	Download
`FP16`	46.7 GB	29.2 GB	19.2 GB	Original weights (opens in a new tab)
`FP8`	30.8 GB	22.0 GB	16.7 GB	Original weights (opens in a new tab)
`Q8 GGUF`	31.3 GB	22.5 GB	16.9 GB	GGUF (opens in a new tab)
`Q4 GGUF`	28.0 GB	19.2 GB	15.7 GB	GGUF (opens in a new tab)

Components

Backbone: 7.12B
VAE: 0.338B
Qwen3-VL 8B: 8.77B

Best hardware for Qwen-Image 2.1 Turbo

The least expensive GPU in our catalog that runs it without offloading is the AMD Radeon RX 7900 XT (launch price $899) at Q4 GGUF.

Quality	Hardware	Quant	Memory	Speed	Runs
87Excellent quality (A+) · 87 of 100: how capable the model is at FP8, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 5090	`FP8`	30.8 GB / 32.0 GB	8.7 s	Runs welltight fit
89Excellent quality (A+) · 89 of 100: how capable the model is at FP16, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA RTX PRO 6000 Blackwell	`FP16`	46.7 GB / 96.0 GB	8.5 s	Runs great
87Excellent quality (A+) · 87 of 100: how capable the model is at FP8, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 4090	`FP8`	22.0 GB / 24.0 GB	11 s	Runs welltight fit · encoder in RAM
89Excellent quality (A+) · 89 of 100: how capable the model is at FP16, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.	AMD Ryzen AI Max+ 395 (128 GB)	`FP16`	46.7 GB / 96.0 GB	22 s	Runs well
87Excellent quality (A+) · 87 of 100: how capable the model is at FP8, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking.	AMD Radeon RX 7900 XTX	`FP8`	22.0 GB / 24.0 GB	12 s	Runs welltight fit · encoder in RAM
89Excellent quality (A+) · 89 of 100: how capable the model is at FP16, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA RTX Spark N1X (6144-core GPU) (128 GB)	`FP16`	46.7 GB / 102 GB	26 s	Runs well
89Excellent quality (A+) · 89 of 100: how capable the model is at FP16, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GB10 (128 GB)	`FP16`	46.7 GB / 115 GB	24 s	Runs well
87Excellent quality (A+) · 87 of 100: how capable the model is at FP8, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 3090	`FP8`	22.0 GB / 24.0 GB	21 s	Runs welltight fit · encoder in RAM
87Excellent quality (A+) · 87 of 100: how capable the model is at FP8, whatever it runs on. Match score 94/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA L40	`FP8`	30.8 GB / 48.0 GB	9.7 s	Runs great
87Excellent quality (A+) · 87 of 100: how capable the model is at FP8, whatever it runs on. Match score 94/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA L40S	`FP8`	30.8 GB / 48.0 GB	9.7 s	Runs great

Check it on your own hardware

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.