Low GPU-Util and Save Promblem During Full training on Qwen-Image-Edit-2509

Hi,

Thanks for your excellect framework!

I’m fine-tuning Qwen-Image-Edit-2509 on a self-built image editing dataset, but the training speed is extremely slow.

Environment: single node with 8 GPUs

Dataset size: ~1M samples

Estimated training time: ~1300 hours (very slow)

As shown in the attached nvitop screenshot, GPU utilization is very low across all devices.

Is this expected for this model, or could there be an inefficiency in data loading / communication?
Any suggestions or optimization tips to improve multi-GPU utilization would be greatly appreciated.

Additionally, When performing full fine-tuning, saving model weights is extremely slow — only one GPU shows high utilization during the saving stage, and the process often triggers NCCL timeout errors, blocking the training.

Is there a recommended way to handle or accelerate checkpoint saving in multi-GPU fine-tuning?

No file, test, or entry point is named. Start by reproducing full fine-tuning on the reported single-node, eight-GPU setup and inspect data loading, communication, and checkpoint-saving stages. Done means identifying the cause of low utilization and NCCL timeouts and documenting a validated optimization or workaround.