ComfyUI documentation
- ComfyUI Server Configuration
This article details the recommended server configurations for running ComfyUI, a powerful and flexible Stable Diffusion UI. It is geared toward users who are familiar with basic server administration concepts and wish to optimize their setup for performance and stability. Proper server configuration is crucial for a smooth ComfyUI experience, especially when utilizing demanding workflows or multiple concurrent users. This guide covers hardware requirements, software dependencies, and suggested optimization strategies.
Hardware Requirements
The hardware requirements for ComfyUI vary greatly depending on the models used, the image resolution, and the number of concurrent users. The following table provides a baseline for different usage scenarios. Note that these are *recommendations* and can be adjusted based on individual needs.
| Usage Scenario | GPU | CPU | RAM | Storage |
|---|---|---|---|---|
| Basic (Testing/Small Images) | NVIDIA GeForce RTX 3060 (12GB VRAM) | Intel Core i5-12400 or AMD Ryzen 5 5600X | 16GB DDR4 | 512GB NVMe SSD |
| Intermediate (1080p/2K Images, Moderate Workloads) | NVIDIA GeForce RTX 3080 (10GB/12GB VRAM) or AMD Radeon RX 6800 XT | Intel Core i7-12700K or AMD Ryzen 7 5800X | 32GB DDR4 | 1TB NVMe SSD |
| Advanced (4K Images, Complex Workflows, Multiple Users) | NVIDIA GeForce RTX 4090 (24GB VRAM) or NVIDIA RTX A6000 | Intel Core i9-13900K or AMD Ryzen 9 7950X | 64GB+ DDR5 | 2TB+ NVMe SSD (RAID 0 recommended) |
GPU Considerations
The GPU is the most critical component for ComfyUI performance. NVIDIA GPUs are generally preferred due to better support for CUDA and optimized drivers. VRAM is particularly important; larger VRAM allows for larger images, more complex workflows, and higher batch sizes. Consider using a dedicated GPU solely for ComfyUI to avoid conflicts with other applications.
CPU and RAM Considerations
While the GPU handles the bulk of the processing, the CPU and RAM play important roles in data loading, pre- and post-processing, and overall system responsiveness. A faster CPU and more RAM will improve workflow speed and reduce bottlenecks.
Storage Considerations
A fast NVMe SSD is highly recommended for storing models, checkpoints, and generated images. The speed of the storage directly impacts loading times and overall performance. Consider using a RAID configuration (e.g., RAID 0) for even faster read/write speeds, but be aware of the increased risk of data loss.
Software Configuration
This section outlines the required software dependencies and configuration steps.
Operating System
Linux (Ubuntu, Debian, or similar) is the recommended operating system for ComfyUI due to its stability, performance, and extensive software support. Windows can also be used, but may require more configuration and may exhibit slightly lower performance.
Python
ComfyUI requires Python 3.10 or 3.11. It's crucial to use a virtual environment to isolate ComfyUI's dependencies from the system's Python installation.
1. Install Python: `sudo apt update && sudo apt install python3.10 python3.10-venv` (Ubuntu example) 2. Create a virtual environment: `python3.10 -m venv .venv` 3. Activate the virtual environment: `source .venv/bin/activate`
Dependencies
Once the virtual environment is activated, install the necessary dependencies using `pip`:
```bash pip install -r requirements.txt ```
(where `requirements.txt` is typically included with the ComfyUI distribution). Common dependencies include `torch`, `torchvision`, `diffusers`, and `xformers`. See the official ComfyUI documentation for the most up-to-date list.
CUDA Toolkit
For NVIDIA GPUs, the CUDA Toolkit is essential. Ensure you install a version of CUDA that is compatible with your GPU driver and PyTorch version. Instructions can be found on the NVIDIA developer website.
ComfyUI Installation
Clone the ComfyUI repository from GitHub:
```bash git clone https://github.com/comfyanonymous/ComfyUI.git cd ComfyUI ```
Running ComfyUI
To start ComfyUI, run the following command from the ComfyUI directory:
```bash python main.py --listen --port 8188 ```
The `--listen` flag allows access from other machines on the network, and `--port` specifies the port number.
Optimization Strategies
Several optimization strategies can improve ComfyUI performance.
| Optimization Strategy | Description | Impact |
|---|---|---|
| xFormers | Enables memory-efficient attention mechanisms, reducing VRAM usage. | Significant (especially for high-resolution images) |
| CUDA Graph Capture | Captures GPU operations into a graph, reducing launch overhead. | Moderate |
| Half-Precision (FP16) | Uses 16-bit floating-point numbers instead of 32-bit, reducing memory usage and potentially increasing speed. | Moderate to Significant |
| Model Optimization | Utilize optimized models (e.g., pruned or quantized) to reduce memory footprint and improve inference speed. | Moderate to Significant |
Monitoring
Use tools like `nvidia-smi` (for NVIDIA GPUs) to monitor GPU utilization, VRAM usage, and temperature. This helps identify bottlenecks and optimize resource allocation. System monitoring tools can also provide insights into CPU and RAM usage.
Networking
If accessing ComfyUI remotely, ensure a stable and high-bandwidth network connection. Consider using a wired connection instead of Wi-Fi for improved reliability. Proper firewall configuration is also important for security.
Workflow Optimization
Simplify your workflows by removing unnecessary nodes or using more efficient alternatives. Experiment with different sampling methods and schedulers to find the optimal settings for your desired results. ComfyUI workflows can be shared and optimized collectively.
Stable Diffusion CUDA Linux Windows Python GitHub NVIDIA developer website ComfyUI documentation System monitoring tools Firewall configuration ComfyUI workflows Virtual environment Model optimization GPU CPU RAM
Intel-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Core i7-6700K/7700 Server | 64 GB DDR4, NVMe SSD 2 x 512 GB | CPU Benchmark: 8046 |
| Core i7-8700 Server | 64 GB DDR4, NVMe SSD 2x1 TB | CPU Benchmark: 13124 |
| Core i9-9900K Server | 128 GB DDR4, NVMe SSD 2 x 1 TB | CPU Benchmark: 49969 |
| Core i9-13900 Server (64GB) | 64 GB RAM, 2x2 TB NVMe SSD | |
| Core i9-13900 Server (128GB) | 128 GB RAM, 2x2 TB NVMe SSD | |
| Core i5-13500 Server (64GB) | 64 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Server (128GB) | 128 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Workstation | 64 GB DDR5 RAM, 2 NVMe SSD, NVIDIA RTX 4000 |
AMD-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Ryzen 5 3600 Server | 64 GB RAM, 2x480 GB NVMe | CPU Benchmark: 17849 |
| Ryzen 7 7700 Server | 64 GB DDR5 RAM, 2x1 TB NVMe | CPU Benchmark: 35224 |
| Ryzen 9 5950X Server | 128 GB RAM, 2x4 TB NVMe | CPU Benchmark: 46045 |
| Ryzen 9 7950X Server | 128 GB DDR5 ECC, 2x2 TB NVMe | CPU Benchmark: 63561 |
| EPYC 7502P Server (128GB/1TB) | 128 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/2TB) | 128 GB RAM, 2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/4TB) | 128 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/1TB) | 256 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/4TB) | 256 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 9454P Server | 256 GB RAM, 2x2 TB NVMe |
Order Your Dedicated Server
Configure and order your ideal server configuration
Need Assistance?
- Telegram: @powervps Servers at a discounted price
⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️