ComfyUI CUDA Out of Memory: Practical Checklist
By Elmehdi ยท Updated October 5, 2026
CUDA out of memory means a GPU allocation failed. A structurally valid workflow can still exceed available memory. The stage that fails matters more than the fact that the workflow loads successfully.
Find the failing stage in the log
Read the lines immediately before the error. Determine whether it happens during model loading, sampling, VAE decoding or an upscale stage. Copy that short excerpt into the OnBeg error log inspector for a pattern check. The tool cannot measure GPU memory or determine which installed component is responsible.
Reduce one source of pressure at a time
- Close other GPU-heavy applications and retry the same workflow.
- Set batch size to one if your workflow allows it.
- Test a lower image resolution while keeping other settings unchanged.
- If the failure occurs during decoding, check whether the VAE node offers a tiled decode option compatible with your workflow.
- Temporarily bypass optional upscale or enhancement stages to isolate their contribution.
Record the change and whether it worked. Several simultaneous changes can hide the actual cause. Do not assume that changing the seed or decreasing sampling steps will solve the allocation problem.
Check compatibility before changing packages
Compare the model and node requirements with the versions in your environment. Avoid blindly reinstalling Python packages: a memory error and a package mismatch are different problems. Follow the relevant model or custom node documentation for supported memory options.
Verify the result
Repeat the reduced workflow, then reintroduce optional stages individually. A successful small render does not prove that your original resolution or batch size fits. Keep the original workflow and an error excerpt so you can explain the failing stage if you ask for help.