Caching to not Waste
This node allows you to cache/caching/store and reuse resized images, ControlNet images, masks, and texts. It avoids repeating heavy operations by loading previously saved files — saving time, memory, and processing power in future executions.
Quick Technical Summary: Caching to not Waste
- Base VRAM Footprint:
- 2048 MB (6 GB Tier)
- Primary Dependencies:
- None (Pure Python/Torch)
- Min PyTorch / CUDA:
- PyTorch 2.0+ | CUDA 12.1+
- GitHub Repository:
- https://github.com/alastor-666-1933/caching_to_not_waste
Citation Note: Data sourced from VRAM DB. For complete workflow OOM estimations, use the VRAM DB Workflow Analyzer.
How much VRAM does Caching to not Waste require?
Direct Answer: The ComfyUI node Caching to not Waste requires a minimum base VRAM of 2048MB and is optimized for GPUs with at least 6GB of VRAM. Low VRAM mode is fully supported for resource-constrained setups.
- Base VRAM:
- 2048MB (2.0GB)
- Recommended GPU:
- 6GB+ VRAM
- Low VRAM Mode:
- ✓ Supported
- Estimation Confidence:
- MEDIUM
Cheapest VRAM Upgrade Paths (Live Market Prices):
- GeForce RTX 3060 12GB (Ultimate Budget VRAM King)──► Used: $209.62View eBay ↗
- GeForce RTX 4060 8GB (Modern Entry-Level)──► New: $303.50View Amazon ↗
Interactive VRAM Compatibility Estimator
Your GPU has plenty of headroom. You can run this node safely with your active configurations!
Verify Compatibility for Your Specific GPU VRAM
Select your graphics card's VRAM capacity to view optimized batch sizes, suggested resolutions, and custom performance tips for Caching to not Waste:
Buy NVIDIA GeForce RTX 3060 (12GB VRAM)
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Frequently Asked Questions
How much VRAM does Caching to not Waste require?
Caching to not Waste requires a minimum of 2048MB (2.0GB) of VRAM for base operation. For optimal performance, a GPU with at least 6GB of VRAM is recommended. This node supports low VRAM mode for resource-constrained setups.
Can I run Caching to not Waste on an RTX 3060, RTX 4070, or RTX 4090?
✅ RTX 3060 (12GB): Yes, fully compatible with 8.8GB headroom. ✅ RTX 4070 (12GB): Yes, fully compatible with 8.8GB headroom. ✅ RTX 4070 Ti (16GB): Yes, fully compatible with 12.4GB headroom. ✅ RTX 4090 (24GB): Yes, fully compatible with 19.6GB headroom
How much VRAM does Caching to not Waste take on an RTX 3060 vs RTX 4090?
On an RTX 3060 (12GB VRAM), Caching to not Waste runs smoothly on an RTX 3060 (12GB) with 8.8GB of headroom. This is sufficient to run the node alongside standard SD 1.5 and SDXL workflows in full precision. On an RTX 4090 (24GB VRAM), the node runs with extreme headroom on an RTX 4090 (24GB) with 19.6GB of dedicated headroom. This allows you to combine the node with massive models (like FLUX.1 Dev, Schnell, or Hunyuan Video) in full precision (FP16) without any offload flags.
How can I reduce VRAM usage when running Caching to not Waste?
Caching to not Waste supports low VRAM mode. To reduce memory usage: (1) Enable --lowvram or --medvram flags in ComfyUI, (2) Reduce batch size to 1, (3) Use fp16 or fp8 precision if supported, (4) Close other GPU applications.
How do I install Caching to not Waste in ComfyUI?
To install Caching to not Waste: (1) Navigate to your ComfyUI/custom_nodes directory, (2) Clone the repository: git clone https://github.com/alastor-666-1933/caching_to_not_waste, (3) Install dependencies: pip install -r requirements.txt (if present), (4) Restart ComfyUI. Alternatively, use ComfyUI Manager for one-click installation.