Hardware Support

ComfyUI supports a wide range of hardware platforms, from high-end NVIDIA GPUs to CPU-only systems. This guide covers installation and configuration for different hardware types.

Supported Hardware

ComfyUI works on:

NVIDIA GPUs (CUDA) - Best performance and compatibility
AMD GPUs (ROCm) - Good performance on supported cards
Intel GPUs (Arc, Xe) - Experimental support via oneAPI/XPU
Apple Silicon (M1/M2/M3/M4) - MPS acceleration support
Ascend NPUs - Huawei Ascend accelerators
Cambricon MLUs - Cambricon machine learning units
Iluvatar Corex - Iluvatar AI accelerators
CPU - Works on any system (slow)

NVIDIA GPUs

NVIDIA GPUs offer the best performance and compatibility with ComfyUI. Requires CUDA-capable hardware.

Requirements

NVIDIA GPU with CUDA compute capability 3.5 or higher
Updated NVIDIA drivers
Python 3.10+ (3.13 recommended)
PyTorch 2.4 or newer

Installation

Install PyTorch with CUDA 13.0 (Recommended)

pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu130

Install PyTorch with CUDA 12.6 (For Older GPUs)

For NVIDIA 10-series and older GPUs:

pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu126

Install PyTorch Nightly (Latest Features)

pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu130

Portable Windows Builds

Pre-built portable packages are available for Windows:

NVIDIA GPUs (CUDA 13.0) - Python 3.13, CUDA 13.0
NVIDIA GPUs (CUDA 12.6) - Python 3.12, supports 10-series GPUs

Update your NVIDIA drivers if ComfyUI doesn’t start. The portable builds come with Python and PyTorch pre-configured.

Recommended Settings by VRAM

24GB+ VRAM
12GB VRAM
8GB VRAM
4-6GB VRAM
<4GB VRAM

Best performance with all models loaded in VRAM:

python main.py --highvram --preview-method taesd

Or for maximum performance:

python main.py --gpu-only --preview-method taesd

Default settings work well:

python main.py --preview-method auto

Use normal VRAM mode:

python main.py --normalvram --preview-method auto

Enable low VRAM mode:

python main.py --lowvram --fp16-vae --preview-method auto

Use extreme memory saving:

python main.py --novram --fp16-vae --fp8_e4m3fn-unet --preview-method latent2rgb

Troubleshooting

”Torch not compiled with CUDA enabled”

Uninstall and reinstall PyTorch:

pip uninstall torch
pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu130

CUDA malloc errors

Disable cudaMallocAsync:

python main.py --disable-cuda-malloc

Out of memory errors

Reduce VRAM usage:

python main.py --lowvram --fp16-vae

AMD GPUs

AMD GPUs are supported through ROCm on Linux and experimental Windows builds for RDNA 3/4.

Linux (Recommended)

Supported on most modern AMD GPUs with ROCm.

Install PyTorch with ROCm 7.1 (Stable)

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm7.1

Install PyTorch with ROCm 7.2 (Nightly)

May have performance improvements:

pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm7.2

Running ComfyUI

python main.py --use-pytorch-cross-attention --preview-method auto

Windows (Experimental)

Experimental support for RDNA 3, 3.5, and 4 GPUs only.

Windows AMD support is experimental and has less hardware compatibility than Linux.

RDNA 3 (RX 7000 Series)

pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx110X-all/

RDNA 3.5 (Strix Halo / Ryzen AI Max+ 365)

pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx1151/

RDNA 4 (RX 9000 Series)

pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx120X-all/

Portable Windows Build

Experimental AMD Portable Build

Unsupported AMD Cards (Linux)

For older RDNA2 or unsupported cards, use the HSA_OVERRIDE_GFX_VERSION environment variable:

# For RX 6700, 6600, and RDNA2 cards
HSA_OVERRIDE_GFX_VERSION=10.3.0 python main.py

# For RX 7600 and RDNA3 cards
HSA_OVERRIDE_GFX_VERSION=11.0.0 python main.py

AMD ROCm Optimizations

Enable experimental memory-efficient attention:

TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 python main.py --use-pytorch-cross-attention

Enable TunableOp for potential speedup (slow first run):

PYTORCH_TUNABLEOP_ENABLED=1 python main.py --use-pytorch-cross-attention

These optimizations are already enabled by default on RDNA3 GPUs.

Intel GPUs

Intel Arc and Xe GPUs are supported via Intel Extension for PyTorch.

Requirements

Intel Arc GPU (A-series) or Intel Xe integrated graphics
Windows or Linux
Python 3.10+

Installation

Install PyTorch XPU (Stable)

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

Install PyTorch XPU (Nightly)

May have performance improvements:

pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/xpu

See the PyTorch XPU documentation for more details.

Running ComfyUI

python main.py --use-pytorch-cross-attention --preview-method auto

Device Selection

Select specific Intel device:

python main.py --oneapi-device-selector "level_zero:0"

Disable IPEX Optimizations

If you encounter issues:

python main.py --disable-ipex-optimize

Apple Silicon (M1/M2/M3/M4)

ComfyUI supports Apple Silicon Macs with MPS (Metal Performance Shaders) acceleration.

Requirements

Mac with Apple Silicon (M1, M2, M3, or M4)
macOS 12.0 or later
Python 3.10+ (3.13 recommended)

Installation

Install PyTorch with MPS support:

pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu

See Apple’s PyTorch guide for installation instructions.

Install ComfyUI dependencies:

pip install -r requirements.txt

Add models to the appropriate folders as described in the installation guide.

Running ComfyUI

python main.py --preview-method auto

ComfyUI automatically detects and uses MPS acceleration on Apple Silicon.

Performance Tips

Unified Memory: Apple Silicon benefits from unified memory architecture
Model Size: Can handle larger models than GPU VRAM alone would suggest
Performance: Expect 2-4x slower than equivalent NVIDIA GPU, but very capable

Recommended Models

Apple Silicon works well with:

SD 1.5 models
SDXL (on M2 Pro/Max/Ultra or M3/M4)
Flux (on M3 Max/Ultra or M4 Max/Ultra with 64GB+ RAM)

Ascend NPUs

Huawei Ascend NPUs are supported via Ascend Extension for PyTorch.

Requirements

Ascend NPU hardware
Supported Linux kernel (see Ascend documentation)
Ascend Basekit (driver, firmware, CANN)

Installation

Install the recommended Linux kernel version from the torch-npu installation page
Install Ascend Basekit (driver, firmware, and CANN) following platform-specific instructions
Install torch-npu packages following the installation guide
Install ComfyUI dependencies:

pip install -r requirements.txt

Running ComfyUI

python main.py

ComfyUI automatically detects and uses Ascend NPUs.

Cambricon MLUs

Cambricon Machine Learning Units are supported via Cambricon Extension for PyTorch.

Requirements

Cambricon MLU hardware
Cambricon CNToolkit
PyTorch with torch_mlu

Installation

Install Cambricon CNToolkit following the installation guide
Install PyTorch with torch_mlu support following the user guide
Install ComfyUI dependencies:

pip install -r requirements.txt

Running ComfyUI

python main.py

Iluvatar Corex

Iluvatar AI accelerators are supported via Iluvatar Extension for PyTorch.

Requirements

Iluvatar Corex hardware
Iluvatar Corex Toolkit

Installation

Install Iluvatar Corex Toolkit following the installation guide
Install ComfyUI dependencies:

pip install -r requirements.txt

Running ComfyUI

python main.py

CPU Only

ComfyUI can run on CPU alone, though it will be significantly slower than GPU acceleration.

Requirements

Any x86_64 or ARM CPU
16GB+ RAM recommended (32GB+ for larger models)
Python 3.10+

Installation

Install PyTorch CPU version:

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

Running ComfyUI

python main.py --cpu --preview-method latent2rgb

Performance Expectations

SD 1.5: 2-5 minutes per image on modern CPUs
SDXL: 10-30 minutes per image
Flux: Not recommended (extremely slow)

Optimization Tips

python main.py --cpu --lowvram --fp16-unet --preview-method latent2rgb

Hardware Selection Guide

Which GPU Should I Buy?

See the ComfyUI GPU recommendations wiki for detailed buying advice.

Quick Recommendations

Budget

NVIDIA RTX 3060 12GB or RTX 4060 Ti 16GBGood for SD 1.5, SDXL, and smaller models.

Enthusiast

NVIDIA RTX 4070 Ti Super 16GB or RTX 4080 16GBGreat for SDXL, Flux schnell, and most workflows.

Professional

NVIDIA RTX 4090 24GB or RTX 6000 Ada 48GBBest for all models including Flux dev, video generation, and heavy workflows.

Server/Multi-GPU

NVIDIA A6000 48GB or H100 80GBEnterprise solutions for maximum performance and multi-user setups.

VRAM Requirements by Model

Model Type	Minimum VRAM	Recommended VRAM
SD 1.5	4GB	8GB
SDXL	6GB	12GB
Flux Schnell	12GB	16GB
Flux Dev	16GB	24GB
Video (SVD)	12GB	24GB
Mochi	20GB	32GB
Hunyuan Video	32GB	60GB

Lower VRAM amounts can work with --lowvram, --novram, and FP8 quantization, but will be slower.

Multi-GPU Setup

ComfyUI supports multi-GPU systems.

Select Primary GPU

# Use GPU 0 as primary, hide other GPUs
python main.py --cuda-device 0

# Use GPU 1 as primary, keep others visible
python main.py --default-device 1

Environment Variables

Alternatively, set CUDA device visibility:

export CUDA_VISIBLE_DEVICES=0,1
python main.py

Benchmarking Your Hardware

To test your hardware performance:

Load a standard workflow (e.g., SDXL text-to-image)
Run with different settings and note generation times:

# Test 1: Default
python main.py

# Test 2: High VRAM
python main.py --highvram

# Test 3: Optimizations
python main.py --use-pytorch-cross-attention

# Test 4: Experimental
python main.py --fast

Compare execution times in the console output

Getting Help

If you encounter hardware-specific issues:

Check the ComfyUI GitHub Issues
Ask in the ComfyUI Discord #help channel
Join the ComfyUI Matrix chat

Provide details about:

Your hardware (GPU model, VRAM, CPU, RAM)
Operating system and version
PyTorch version (python -c "import torch; print(torch.__version__)")
Full error messages and console output

​Supported Hardware

​NVIDIA GPUs

​Requirements

​Installation

​Install PyTorch with CUDA 13.0 (Recommended)

​Install PyTorch with CUDA 12.6 (For Older GPUs)

​Install PyTorch Nightly (Latest Features)

​Portable Windows Builds

​Recommended Settings by VRAM

​Troubleshooting

​”Torch not compiled with CUDA enabled”

​CUDA malloc errors

​Out of memory errors

​AMD GPUs

​Linux (Recommended)

​Install PyTorch with ROCm 7.1 (Stable)

​Install PyTorch with ROCm 7.2 (Nightly)

​Running ComfyUI

​Windows (Experimental)

​RDNA 3 (RX 7000 Series)

​RDNA 3.5 (Strix Halo / Ryzen AI Max+ 365)

​RDNA 4 (RX 9000 Series)

​Portable Windows Build

​Unsupported AMD Cards (Linux)

​AMD ROCm Optimizations

​Intel GPUs

​Requirements

​Installation

​Install PyTorch XPU (Stable)

​Install PyTorch XPU (Nightly)

​Running ComfyUI

​Device Selection

​Disable IPEX Optimizations

​Apple Silicon (M1/M2/M3/M4)

​Requirements

​Installation

​Running ComfyUI

​Performance Tips

​Recommended Models

​Ascend NPUs

​Requirements

​Installation

​Running ComfyUI

​Cambricon MLUs

​Requirements

​Installation

​Running ComfyUI

​Iluvatar Corex

​Requirements

​Installation

​Running ComfyUI

​CPU Only

​Requirements

​Installation

​Running ComfyUI

​Performance Expectations

​Optimization Tips

​Hardware Selection Guide

​Which GPU Should I Buy?

​Quick Recommendations

Budget

Enthusiast

Professional

Server/Multi-GPU

​VRAM Requirements by Model

​Multi-GPU Setup

​Select Primary GPU

​Environment Variables

​Benchmarking Your Hardware

​Getting Help

Supported Hardware

NVIDIA GPUs

Requirements

Installation

Install PyTorch with CUDA 13.0 (Recommended)

Install PyTorch with CUDA 12.6 (For Older GPUs)

Install PyTorch Nightly (Latest Features)

Portable Windows Builds

Recommended Settings by VRAM

Troubleshooting

”Torch not compiled with CUDA enabled”

CUDA malloc errors

Out of memory errors

AMD GPUs

Linux (Recommended)

Install PyTorch with ROCm 7.1 (Stable)

Install PyTorch with ROCm 7.2 (Nightly)

Running ComfyUI

Windows (Experimental)

RDNA 3 (RX 7000 Series)

RDNA 3.5 (Strix Halo / Ryzen AI Max+ 365)

RDNA 4 (RX 9000 Series)

Portable Windows Build

Unsupported AMD Cards (Linux)

AMD ROCm Optimizations

Intel GPUs

Requirements

Installation

Install PyTorch XPU (Stable)

Install PyTorch XPU (Nightly)

Running ComfyUI

Device Selection

Disable IPEX Optimizations

Apple Silicon (M1/M2/M3/M4)

Requirements

Installation

Running ComfyUI

Performance Tips

Recommended Models

Ascend NPUs

Requirements

Installation

Running ComfyUI

Cambricon MLUs

Requirements

Installation

Running ComfyUI

Iluvatar Corex

Requirements

Installation

Running ComfyUI

CPU Only

Requirements

Installation

Running ComfyUI

Performance Expectations

Optimization Tips

Hardware Selection Guide

Which GPU Should I Buy?

Quick Recommendations

VRAM Requirements by Model

Multi-GPU Setup

Select Primary GPU

Environment Variables

Benchmarking Your Hardware

Getting Help