ComfyUI Any-Device Offload
This custom node gives you total control over where your models run and where they live in memory. It solves common Out of Memory (OOM) errors, enables multi-GPU workflows, and fixes persistent crashes when trying to run modern workflows (like Flux/SD3) on CPUs or secondary GPUs.
Quick Technical Summary: ComfyUI Any-Device Offload
- Base VRAM Footprint:
- 12288 MB (24 GB Tier)
- Primary Dependencies:
- gc, sys, torch, types
- Min PyTorch / CUDA:
- PyTorch 2.0+ | CUDA 12.1+
- GitHub Repository:
- https://github.com/FearL0rd/ComfyUI-AnyDeviceOffload
Citation Note: Data sourced from VRAM DB. For complete workflow OOM estimations, use the VRAM DB Workflow Analyzer.
How much VRAM does ComfyUI Any-Device Offload require?
Direct Answer: The ComfyUI node ComfyUI Any-Device Offload requires a minimum base VRAM of 12288MB and is optimized for GPUs with at least 24GB of VRAM. Low VRAM mode is not supported for this node.
- Base VRAM:
- 12288MB (12.0GB)
- Recommended GPU:
- 24GB+ VRAM
- Low VRAM Mode:
- ✗ Not supported
- Estimation Confidence:
- HIGH
Run ComfyUI Any-Device Offload locally
Honest estimate: this node typically needs ≥ 24GB VRAM in a real workflow.
Cheapest VRAM upgrade paths (live market prices)
- GeForce RTX 3090 24GB (Best Used 24GB Value)Used: $624.42View eBay ↗
- GeForce RTX 4090 24GB (Ultimate AI Beast)New: $1802.60View Amazon ↗
Disclosure: As an Amazon Associate we earn from qualifying purchases. Cloud provider links are referrals at no additional cost to you.
Interactive VRAM Compatibility Estimator
Your GPU has plenty of headroom. You can run this node safely with your active configurations!
Struggling with VRAM Crashes or Out-of-Memory Errors?
Running heavy models like FLUX or SDXL with ComfyUI Any-Device Offload? If you experience torch.cuda.OutOfMemoryError or driver timeouts, read our comprehensive troubleshooting guide to configure launch flags, enable tiled decoding, and avoid crashes.
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 ComfyUI Any-Device Offload:
Local GPU Upgrade vs. Cloud Rental
Compare the real financial break-even point for ComfyUI generation.
Buy NVIDIA GeForce RTX 4090
- Card Purchase Price:
- $1800.00
- Active Wattage Draw:
- 450 Watts
- Annual Electric Cost:
- $0.00
Rent GPU On-Demand
- Estimated Cloud Rate:
- $0.44 / hr
- Weekly Cloud Billing:
- $0.00
- Annual Cost (Equivalent):
- $0.00
What Python packages are required for ComfyUI Any-Device Offload?
Direct Answer: Running ComfyUI Any-Device Offload requires installing the following Python package dependencies: gc, sys, torch, types. Ensure your ComfyUI environment has these packages active before launching.
gc
sys
torch
typesInteractive Setup & Dependency Resolver
# Loading command...Universal Architecture Compatibility
ComfyUI Any-Device Offload operates as a general-purpose processor without binding directly to a single checkpoint architecture. It can be paired across Stable Diffusion 1.5, SDXL, FLUX, and video diffusion pipelines.
Compatible Foundations
This node is verified to support or optimize workflows for the following foundation model families:
Frequently Asked Questions
How much VRAM does ComfyUI Any-Device Offload require?
ComfyUI Any-Device Offload requires a minimum of 12288MB (12.0GB) of VRAM for base operation. For optimal performance, a GPU with at least 24GB of VRAM is recommended. Low VRAM mode is not supported for this node.
Can I run ComfyUI Any-Device Offload on an RTX 3060, RTX 4070, or RTX 4090?
❌ RTX 3060 (12GB): Insufficient VRAM (needs 12.0GB minimum). ❌ RTX 4070 (12GB): Insufficient VRAM (needs 12.0GB minimum). ⚠️ RTX 4070 Ti (16GB): Can run, but may experience performance issues or require low VRAM mode. ✅ RTX 4090 (24GB): Yes, fully compatible with 9.6GB headroom
How much VRAM does ComfyUI Any-Device Offload take on an RTX 3060 vs RTX 4090?
On an RTX 3060 (12GB VRAM), ComfyUI Any-Device Offload will struggle or run out of memory on an RTX 3060 (12GB) without aggressive memory offloading (using --lowvram), as the node's base footprint of 12.0GB takes up a large portion of the card's capacity. On an RTX 4090 (24GB VRAM), the node runs with extreme headroom on an RTX 4090 (24GB) with 9.6GB 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.
What PyTorch version does ComfyUI Any-Device Offload need?
ComfyUI Any-Device Offload requires the following PyTorch-related packages: torch. Ensure your ComfyUI environment has these installed. Ensure your PyTorch installation matches your CUDA version (use torch.version.cuda to check).
What Python packages are required for ComfyUI Any-Device Offload?
To run ComfyUI Any-Device Offload, you need to install: gc, sys, torch, types. You can install these using pip or add them to your requirements.txt file.
How do I install ComfyUI Any-Device Offload in ComfyUI?
To install ComfyUI Any-Device Offload: (1) Navigate to your ComfyUI/custom_nodes directory, (2) Clone the repository: git clone https://github.com/FearL0rd/ComfyUI-AnyDeviceOffload, (3) Install dependencies: pip install -r requirements.txt (if present), (4) Restart ComfyUI. Alternatively, use ComfyUI Manager for one-click installation.
Are you the author of this node?
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