ComfyUI Out of Memory: Fix CUDA OOM, Python MemoryError, and VRAM Spikes

If ComfyUI fails with out of memory, do not start by reinstalling packages. First identify which memory pool is actually exhausted.

ComfyUI can run out of:

• GPU VRAM (CUDA out of memory, torch.cuda.OutOfMemoryError)
• system RAM (MemoryError, DefaultCPUAllocator: not enough memory)
• Apple unified memory (MPS backend out of memory)
• allocator-friendly contiguous blocks, even when a monitor appears to show free memory

The fix is different for each case.

Quick Diagnosis

Evidence from real ComfyUI issues

This page is prioritized because memory failures are a real support pattern, not a theoretical edge case.

In the local ComfyUI GitHub Issues dataset collected on 2026-03-19, the OutOfMemoryError | out of memory cluster had 203 issues, 99 still open, and a latest seen date of 2026-03-19. The same dataset also shows related clusters for allocation would exceed allowed memory, cuda out of memory, and MPS backend out of memory.

Our local telemetry join also ranked CUDA out of memory and Python MemoryError among the strongest service opportunities. Use those signals as demand evidence, then use the checks below to choose the right fix.

Step 1: Check GPU VRAM

On Windows, open Command Prompt while ComfyUI is running:

nvidia-smi

Look for:

• another AI app using VRAM
• browser or game processes using GPU memory
• ComfyUI filling nearly all VRAM before failure
• a second ComfyUI instance still running

If you have multiple NVIDIA GPUs, confirm ComfyUI is using the intended card. See ComfyUI Multi GPU before changing devices.

Step 2: Check the active Python environment

Run this in the same Python environment that launches ComfyUI:

python -c "import torch; print('cuda', torch.cuda.is_available()); print(torch.cuda.mem_get_info() if torch.cuda.is_available() else 'no cuda')"

If CUDA is available, this prints free and total VRAM from PyTorch's view. For deeper inspection:

python -c "import torch; print(torch.cuda.memory_summary() if torch.cuda.is_available() else 'no cuda')"

For the Windows portable package, use the embedded Python:

.\python_embeded\python.exe -s -c "import torch; print('cuda', torch.cuda.is_available()); print(torch.cuda.mem_get_info() if torch.cuda.is_available() else 'no cuda')"

If CUDA is not available at all, this is not an OOM problem. Use Torch Not Compiled With CUDA Enabled first.

Step 3: Reduce the peak allocation

Most ComfyUI OOM failures happen at a peak point: model load, VAE decode, upscale, video sampling, or a custom node that creates a large tensor.

Try these in order:

1. Reduce output resolution.
2. Set batch size to 1.
3. For video, reduce frame count before reducing quality settings.
4. Use a smaller checkpoint or a quantized/GGUF model when available.
5. Avoid running two large branches in one workflow if they can be split into stages.
6. Save intermediate images or latents, then continue in a second workflow.

If you are using Flux, Wan, HunyuanVideo, LTX, or other large modern models, the model choice itself may exceed your hardware. Check GPU Compatibility and Model Types Explained before trying random package changes.

Step 4: Use launch flags carefully

For manual installs:

python main.py --lowvram

For portable Windows, edit the .bat file and add the flag after main.py:

.\python_embeded\python.exe -s ComfyUI\main.py --lowvram

If the OS or another app needs more headroom, reserve VRAM:

python main.py --lowvram --reserve-vram 1.0

Useful flags:

Do not add every memory flag at once. Change one thing, restart ComfyUI, and test the same workflow.

Step 5: Distinguish VRAM from system RAM

CUDA out of memory usually means GPU VRAM. MemoryError and DefaultCPUAllocator usually mean system RAM.

For system RAM pressure:

• close browsers, video editors, games, and other AI tools
• increase the Windows page file if the machine is paging heavily
• avoid loading several large models in one workflow
• split long video workflows into shorter chunks
• avoid huge input images unless the model actually needs them

If RAM rises after installing a custom node, disable that node and retest. See Installed Custom Nodes and Broke ComfyUI.

What not to do

• Do not reinstall PyTorch just because the error says OOM.
• Do not assume torch.cuda.empty_cache() fixes a workflow that genuinely needs more peak memory.
• Do not use --highvram on a low-VRAM machine; it keeps models resident in GPU memory.
• Do not install memory-optimization custom nodes while the base workflow is still untested.
• Do not update all custom nodes during an urgent OOM repair.

When an update caused the OOM

If the same workflow worked yesterday and now OOMs after an update:

1. Save the current workflow JSON.
2. Confirm whether ComfyUI, the frontend, or a custom node changed.
3. Test the same workflow with custom nodes disabled if possible.
4. Try a smaller model/resolution only to confirm the failure type.
5. Restore a backup or downgrade in a separate folder if production work is blocked.

Use ComfyUI Won't Update or Need to Downgrade before rolling back the only working environment.

How Wonderful Launcher helps

Out-of-memory failures become expensive when users lose track of which workflow, model, custom node, and ComfyUI version caused the spike.

Wonderful Launcher helps by keeping environments separate, preserving launch paths, and making it easier to inspect logs after a crash. If the machine is stuck in repeated OOM, dependency, or update failures, expert help is usually faster than trying random flags for hours.

Source references

• ComfyUI command-line arguments source
• PyTorch CUDA semantics and memory management
• PyTorch CUDA memory summary
• PyTorch empty_cache documentation
• ComfyUI issue example: OOM killer with LTX workflow
• ComfyUI issue example: CUDA OOM despite visible free memory

Related guides

• ComfyUI GPU Compatibility
• ComfyUI Multi GPU
• Torch Not Compiled With CUDA Enabled
• ComfyUI Dependency Conflicts
• ComfyUI Won't Update or Need to Downgrade

Source References

• ComfyUI system requirements
• ComfyUI command-line arguments source
• ComfyUI issue example: OOM killer with LTX workflow
• ComfyUI issue example: CUDA OOM despite visible free memory