Fixing GPU Errors, VRAM Issues, and CUDA Problems

The complete solution guide for hardware and driver issues

#Overview

GPU and VRAM errors are the #2 most common problem in ComfyUI and Stable Diffusion (after installation issues). This guide solves every major GPU-related error reported on Reddit.

#🔴 Error: "CUDA not available" or "Device: cpu"

Symptoms

• →Console shows Device: cpu instead of Device: cuda
• →Generation is extremely slow
• →Error message: "CUDA not available"

Root Cause

PyTorch installed without CUDA support, or incorrect CUDA version

Solution

Step 1: Check Your GPU

Windows:

Expected output:

Note your CUDA Version (e.g., 12.1)

Step 2: Reinstall PyTorch with CUDA

Uninstall current PyTorch:

Install correct CUDA version:

For CUDA 12.1:

For CUDA 11.8:

Step 3: Verify

Restart ComfyUI. Console should now show:

#🔴 Error: "CUDA out of memory" (OOM)

Symptoms

• →Error: RuntimeError: CUDA out of memory
• →Generation crashes mid-process
• →Can't complete workflows that previously worked

Understanding VRAM

The table below is a troubleshooting starting point, not a compatibility matrix or a promise that a particular model will run. Resolution, precision, batch size, offloading, custom nodes, and the graph can change memory use materially.

Solution 1: Reduce Image Resolution

Current workflow using 1024×1024?

Try:

• →768×768 (SDXL)
• →512×512 (SD 1.5)

How to change: Find the "Empty Latent Image" node → Change width and height

Solution 2: Enable Low VRAM Mode

For 4-8GB GPUs:

Run ComfyUI with:

For very low VRAM (< 4GB):

Portable version: Edit run_nvidia_gpu.bat, add --lowvram:

Solution 3: Use Tiled VAE

Install ComfyUI-TiledKSampler custom node:

1. →Go to ComfyUI/custom_nodes
2. →Clone:
   comfyui-workflow.json

   git clone https://github.com/shiimizu/ComfyUI-TiledKSampler
3. →Restart ComfyUI
4. →Replace VAE Decode node with Tiled VAE Decode

Result: Can reduce memory pressure for some graphs; measure the result on the exact workflow instead of assuming a percentage.

Solution 4: Reduce Batch Size

Find any node with batch_size → Set to 1

Example:

Solution 5: Use Lower Precision Models

SD 1.5 models:

• →Use fp16 versions instead of fp32
• →Half the VRAM usage

Where to find: Model filename contains:

• →fp16 ← Use this
• →fp32 ← Avoid on low VRAM

#🔴 Mac GPU Not Being Used (M1/M2/M3)

Symptoms

• →Generation extremely slow on Mac
• →Console shows Device: cpu
• →Activity Monitor shows low GPU usage

Solution

Force MPS (Metal Performance Shaders):

For maximum performance:

Verify: Console should show:

#🔴 Error: "RuntimeError: No CUDA GPUs are available"

Cause

• →GPU driver not installed
• →Driver outdated
• →GPU not detected by system

Solution

Step 1: Update GPU Drivers

NVIDIA:

1. →Go to NVIDIA Driver Downloads
2. →Select your GPU model
3. →Download and install latest Game Ready or Studio driver

AMD (Linux):

Step 2: Verify Driver

Windows/Linux:

Should show your GPU and driver version

Step 3: Restart

Fully restart your computer after driver installation

#🔴 Error: "Torch not compiled with CUDA enabled"

Cause

PyTorch CPU-only version installed

Solution

Complete reinstall:

Verify CUDA is available:

Expected output: True

#🔴 Black Images / Empty Output

Cause

Often related to VAE or precision issues on certain GPUs

Solution 1: Change VAE

1. →Download vae-ft-mse-840000-ema-pruned.safetensors
2. →Place in ComfyUI/models/vae/
3. →In workflow, add "VAE Loader" node
4. →Connect to "VAE Decode"

Solution 2: Force FP32 Precision

For Mac:

#🔴 Very Slow Generation (Multiple Minutes Per Image)

Diagnostic Checklist

1. →

   Is GPU being used?

   • →Check console: Should say Device: cuda or Device: mps
   • →If says Device: cpu → See "CUDA not available" above
2. →

   Is resolution too high for your GPU?

   • →See VRAM table above
   • →Reduce resolution
3. →

   Are you using too many steps?

   • →20-30 steps is usually enough
   • →Reduce from 50+ → 25
4. →

   Is xformers enabled?

   comfyui-workflow.json

   pip install xformers
5. →

   Too many upscale passes?

   • →Remove or reduce upscaling nodes

#🔴 Error: "CUDA error: device-side assert triggered"

Cause

Usually model/LoRA incompatibility or corrupted model file

Solution

1. →Remove all LoRAs from workflow
2. →Try different checkpoint
3. →Re-download suspected corrupt models
4. →Check model compatibility:
   • →SD 1.5 LoRA → SD 1.5 checkpoint only
   • →SDXL LoRA → SDXL checkpoint only

#Performance Optimization Quick Reference

For 4-6GB VRAM (GTX 1660, RTX 3050)

• →Use SD 1.5 only
• →Max 512×512 resolution
• →Install Tiled VAE
• →Batch size: 1

For 8-12GB VRAM (RTX 3060, RTX 4060)

• →SDXL supported at 768×768
• →SD 1.5 at 768×768
• →Batch size: 1-2

For 16-24GB VRAM (RTX 4080, RTX 4090)

• →SDXL at 1024×1024+
• →High-resolution workflows
• →Batch size: 2-4
• →Video generation supported

#🆘 Emergency Reset

If nothing works:

1. →

   Completely uninstall PyTorch:

   comfyui-workflow.json

   pip uninstall torch torchvision torchaudio pip cache purge
2. →

   Update GPU drivers (see above)

3. →

   Restart computer

4. →

   Fresh PyTorch install:

   comfyui-workflow.json

   pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
5. →

   Verify:

   comfyui-workflow.json

   python -c "import torch; print(torch.cuda.is_available()); print(torch.version.cuda)"

#🎯 Related Guides

• →Guide 7: Performance Optimization
• →Guide 8: Fix Missing Dependencies
• →Guide 9: Troubleshooting Assistant

Still having GPU issues? Check the Troubleshooting Assistant for interactive diagnosis.