Fix torch.cuda.OutOfMemoryError in ComfyUI on 8GB and 12GB NVIDIA GPUs
A torch.cuda.OutOfMemoryError in ComfyUI happens when the diffusion checkpoint, T5/CLIP text encoders, VAE decode buffers, and Windows WDDM display reserve exceed physical NVIDIA VRAM. Fix it immediately by switching from FP16 weights to quantized GGUF Q4_K_S (6.8 GB), passing –lowvram or –reserve-vram 1.5, and enabling tiled VAE decoding.
1. Exact Terminal Error Signature & Stack Trace
If your standalone ComfyUI, Ollama, or Python terminal outputs the following error on Windows 10/11 x64 or Linux x64:
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB (GPU 0; 8.00 GiB total capacity; 6.84 GiB already allocated; 0 bytes free; 7.12 GiB reserved in total by PyTorch)2. Root Cause in Standalone Python, ComfyUI, or Ollama
[Verified] Full-precision FP16 FLUX checkpoints require 23.8 GB for the transformer alone plus 9.5 GB for the T5-XXL text encoder. Even when running quantized 6.8 GB GGUF Q4_K_S weights on an 8 GB or 12 GB GPU, keeping T5-XXL pinned in VRAM during sampling or decoding a 1024×1024 latent in FP32 without tiling pushes peak allocation past physical memory.
3. Manual CLI Fix for Standalone Users (100% Free Utility)
Run the following commands in your terminal or update your launch configuration to resolve the issue immediately in a standalone installation:
# 1. Launch standalone ComfyUI with sequential VRAM offload and a 1.5 GB OS safety reserve:
python main.py --lowvram --reserve-vram 1.5 --use-split-cross-attention
# 2. Replace 23.8 GB FP16 checkpoints with quantized GGUF Q4_K_S (6.8 GB) inside models/unet/:
# Use UnetLoaderGGUF + DualCLIPLoaderGGUF so the text encoder unloads to system RAM before sampling.
# 3. If the crash occurs at 95%-100% progress (during VAE decode), replace VAEDecode with VAEDecodeTiled (tile_size=512).4. How Logen 1.4.0 Prevents This Error Automatically
Logen 1.4.0 prevents CUDA out-of-memory crashes through three automated mechanisms: (1) its Setup Wizard provisions quantized FLUX.1-schnell GGUF Q4_K_S (6.8 GB) by default instead of 24 GB FP16 weights, (2) it issues keep_alive=0 to Ollama before launching ComfyUI so chat LLM weights never compete for VRAM, and (3) if a high-resolution run triggers vram_oom, Logen surfaces a one-click recovery banner that switches to tiled VAE decoding while keeping your prompt intact.
Supported platforms: Windows 10/11 x64 (Logen-1.4.0-Setup.exe) and Linux x64 (Logen-1.4.0.AppImage, Logen-1.4.0-amd64.deb). macOS is not supported.
5. Check Your GPU & Model Memory Headroom
Calculate Exact Dedicated VRAM Headroom on Windows 10/11 & Linux x64
Select your NVIDIA graphics card, diffusion model, operating system, and target resolution to compute deterministic peak VRAM usage before you download weights.
Formula: Dedicated_VRAM (12.0 GB) – [Weights (6.8 GB) + Encoder (1.2 GB) + VAE Buffer (0.9 GB) + OS Reserve (1.5 GB)] = +1.60 GB available headroom.
6. Troubleshooting FAQ: Fix torch.cuda.OutOfMemoryError in ComfyUI on 8GB and 12GB NVIDIA GPUs
Why does ComfyUI crash with CUDA out of memory at 98% progress?
A crash right at the end of sampling means the UNet finished denoising, but the VAE decoder ran out of contiguous VRAM while expanding the 128×128 latent tensor into a 1024×1024 RGB image. Switching to VAEDecodeTiled (512px tiles) or freeing background GPU memory fixes it.
What is the difference between –lowvram and –medvram in ComfyUI?
–medvram keeps the diffusion transformer in VRAM while offloading the CLIP and T5 text encoders to system RAM after prompt encoding. –lowvram goes further by streaming individual transformer blocks between system RAM and GPU memory when dedicated VRAM is 6 GB to 8 GB.
How does Logen 1.4.0 stop VRAM crashes without manual launch flags?
Logen 1.4.0 inspects your NVIDIA GPU VRAM before downloading models, selects quantized GGUF Q4_K_S or FP8 checkpoints that fit your card, unloads Ollama chat weights before diffusion sampling, and provides one-click vram_oom recovery in the desktop UI.
7. Related Local AI & ComfyUI Error Guides
- Fix Prompt Outputs Failed Validation: DualCLIPLoader / VAELoader Not Found in ComfyUI (Missing Nodes & Checkpoints)
- Fix AssertionError: Torch Not Compiled with CUDA Enabled on Windows 10/11 (Python & CUDA Environment)
- Fix CUDA Out of Memory When Running Ollama and ComfyUI Together (VRAM & OOM)
- Fix Cannot Import ComfyUI-GGUF Module: No Module Named 'gguf' (Missing Nodes & Checkpoints)
- Fix Windows 10/11 WDDM Shared GPU Memory Slowdown (Why FLUX Drops from 25s to 4 Minutes) (VRAM & OOM)
- Fix Error: connect ECONNREFUSED 127.0.0.1:11434 (WinError 10061) in Local AI Apps (Ollama & Background Services)
- Fix Black Image or NaN Tensor Output When Decoding FLUX VAE in ComfyUI (VAE & Precision Errors)
- Fix dlopen(): error loading libfuse.so.2 When Launching AppImage on Ubuntu 22.04 / 24.04 (Linux Desktop & Packaging)
- Fix OutOfMemoryError During 4x RealESRGAN 4K Upscaling in ComfyUI (VRAM & OOM)
Checking hardware compatibility? Browse the Local AI GPU & VRAM Compatibility Index or explore Logen 1.4.0 Desktop Studio.
Generate with FLUX.1-schnell, FLUX.2 [klein] 4B & Z-Image-Turbo in a self-healing desktop studio.
Logen 1.4.0 manages ComfyUI and Ollama automatically on your NVIDIA GPU with zero monthly fees.