ComfyUI CUDA Out of Memory Fix: torch.cuda.OutOfMemoryError

If you searched for ComfyUI CUDA out of memory, torch.cuda.OutOfMemoryError: CUDA out of memory, or ComfyUI out of memory fix, your GPU does not have enough VRAM for the current operation.

This is usually not a broken installation. It means the generation settings, model size, loaded extras, or resolution exceed what your GPU can hold in memory at once. The good news is that most OOM errors can be fixed without buying a new GPU.

The OOM cases we see most often

In real support and launcher telemetry, CUDA OOM usually appears in one of these patterns:

What the error looks like

torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB.
GPU 0 has a total capacity of 8.00 GiB of which 512.00 MiB is free.
Including non-PyTorch memory, this process has 7.48 GiB memory in use.

You may also see variations like:

RuntimeError: CUDA error: out of memory

Error occurred when executing KSampler:
CUDA out of memory.

Not enough GPU memory. Try reducing image resolution or batch size and run again.

The error typically occurs during sampling, VAE decoding, or model loading. The "not enough GPU memory" message is ComfyUI's friendly rendering of the same CUDA OOM condition — the fix is identical: lower resolution, batch size, or frame count before changing any packages.

Why it happens

• Resolution too high: Larger images use exponentially more VRAM. A 2048x2048 image uses roughly 4x the VRAM of a 1024x1024 image
• Model too large for your GPU: SDXL models need more VRAM than SD 1.5 models. FLUX models need even more
• Multiple models loaded: Loading a checkpoint, ControlNet, IP-Adapter, and LoRA simultaneously adds up
• Batch size too high: Generating multiple images at once multiplies VRAM usage
• VAE decode at full resolution: The VAE decode step can spike VRAM even when sampling succeeded
• Other programs using GPU memory: Chrome, Discord, or other GPU-accelerated apps consume VRAM

Before you change anything

Check whether the error is really CUDA OOM and not another runtime issue:

• CUDA out of memory usually means the workflow is too large for current VRAM
• Torch not compiled with CUDA enabled is a different problem
• CUDA is not available is a runtime/configuration problem
• Python MemoryError usually points to system RAM, not GPU VRAM

If the wording changed after a restart, always follow the newest first error.

Classify the memory pool first

Different memory errors need different fixes. If the repair path starts from the wrong pool, it can waste time or make the environment noisier.

Do not reinstall PyTorch just because the word "memory" appears in the log. Reinstalling packages is only relevant if the newest error says CUDA is unavailable, Torch is CPU-only, or a dependency import failed.

VRAM requirements by model type

Step-by-step fixes

1. Lower resolution and batch size

The fastest fix. Reduce your image dimensions:

• SD 1.5: try 512x512 or 512x768
• SDXL: try 768x768 or 832x1216
• FLUX: try 768x768 first

Set batch size to 1 if it is higher.

2. Use ComfyUI memory management flags

Launch ComfyUI with memory-saving arguments:

.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --lowvram

Available flags:

2.5 Reduce loaded extras before changing core runtime

If you are using a large workflow, remove one class of extra model at a time and retry:

• extra ControlNet branches
• multiple LoRAs
• IP-Adapter stacks
• upscalers loaded in the same run
• large video or high-frame workflows

This usually tells you faster whether the limit is coming from the base model or from stacked conditioning.

3. Use quantized or fp16 models

Full fp32 models use twice the VRAM of fp16 models. fp8 models use even less:

• Download fp16 checkpoints when available
• For FLUX, use fp8 quantized checkpoints on 8 GB GPUs
• Some checkpoint sites label these as "fp16-fix" or "pruned"

4. Enable tiled VAE decode

If the OOM happens during VAE decode (the final step), add a VAE Decode (Tiled) node instead of the regular VAE Decode. Tiled decoding processes the image in smaller chunks.

5. Close other GPU applications

Check what else is using VRAM:

nvidia-smi

Close browsers, games, video editors, or other applications that consume GPU memory before generating.

6. Use --force-fp16 for the entire pipeline

.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --force-fp16

This forces all operations to use fp16 precision, roughly halving VRAM usage at the cost of minor quality differences.

What not to do

• Do not increase Windows virtual memory (pagefile) thinking it will fix GPU OOM — VRAM and system RAM are separate
• Do increase page file only when the error is system RAM, commit limit, MemoryError, or DefaultCPUAllocator, not when the traceback is clearly CUDA VRAM
• Do not reinstall ComfyUI — this is a resource limit, not a broken install
• Do not install random CUDA toolkits — the PyTorch wheel includes what it needs
• Do not ignore the specific numbers in the error message — they tell you exactly how much VRAM you have and how much was requested

When OOM is a hardware limit, not a bug

Sometimes the environment is fine and the workflow simply exceeds the card:

• 6 GB cards often need more conservative SDXL settings
• 8 GB cards often need quantized FLUX or smaller resolutions
• video pipelines can exceed 12 GB very quickly

In those cases, the fix is workflow sizing, quantization, tiling, or a different model family — not reinstalling ComfyUI.

How Wonderful Launcher can help

Wonderful Launcher can detect your GPU's VRAM capacity and help identify and suggest memory optimization settings. It also helps manage model formats and suggests compatible settings for your hardware.

Download Wonderful Launcher — it's free and can optimize your ComfyUI setup for your specific GPU.

Related errors

• ComfyUI GPU Compatibility
• Python Out of Memory in ComfyUI
• ComfyUI System Requirements
• Torch Not Compiled With CUDA Enabled
• ComfyUI Common Issues

Source References

• PyTorch CUDA Semantics
• ComfyUI CLI Arguments
• NVIDIA CUDA Documentation