# Hardware Requirements

Invoke runs on Windows 10+, macOS 14+ and Linux (Ubuntu 20.04+ is well-tested).

## Hardware

Hardware requirements vary significantly depending on model and image output size. The requirements below are rough guidelines for best performance. GPUs with less VRAM typically still work, if a bit slower. Follow the [Low VRAM Guide](/content/configuration/low-vram-mode/index.html) to optimize performance.

- All Apple Silicon (M1, M2, etc) Macs work, but 16GB+ memory is recommended.
- AMD GPUs are supported on Linux only. The VRAM requirements are the same as Nvidia GPUs.
- Linux ARM64 (`aarch64`) devices — e.g. Raspberry Pi 5, other SBCs, ARM servers — are supported in CPU-only mode. Local generation is slow without a GPU, but API-backed models (e.g. GPT Image, Gemini) work well.

### Windows/Linux

| Model Family | Best resolution | GPU (series) | VRAM (min) | RAM (min) | Notes |
| --- | --: | --- | --: | --: | --- |
| SD1.5 | 512x512 | Nvidia 10xx+ | 4GB | 8GB |  |
| SDXL | 1024x1024 | Nvidia 20xx+ | 8GB | 16GB |  |
| FLUX.1 | 1024x1024 | Nvidia 20xx+ | 10GB | 32GB |  |
| FLUX.2 Klein 4B | 1024x1024 | Nvidia 30xx+ | 12GB | 16GB | FP8 works with 8GB+; Diffusers + encoder |
| FLUX.2 Klein 9B | 1024x1024 | Nvidia 40xx | 24GB | 32GB | FP8 works with 12GB+; Diffusers + encoder |
| Z-Image Turbo | 1024x1024 | Nvidia 20xx+ | 8GB | 16GB | Q4_K 8GB; Q8/BF16 16GB+ |
| Krea-2 (Turbo / Raw) | 1024x1024 | Nvidia 40xx | 24GB | 32GB | FP8 works with 16GB+; GGUF Q4_K ~12GB. Diffusers ~26GB; GGUF needs a standalone VAE + Qwen3-VL encoder |
| Wan 2.2 A14B (T2V/I2V) | 1280x720 | Nvidia 30xx+ | 12GB | 32GB | Dual-expert MoE; Q4_K_M 12GB; Q8 18GB+; Diffusers requires 32GB+ |
| Wan 2.2 TI2V-5B | 1280x720 | Nvidia 20xx+ | 8GB | 16GB | Single transformer; Q4_K_M 6GB+; Q8 8GB+; Diffusers 12GB+ |

## Python

Invoke requires python `3.11` through `3.12`. If you don’t already have one of these versions installed, we suggest installing `3.12`, as it will be supported for longer.

Check that your system has an up-to-date Python installed by running `python3 --version` in the terminal (Linux, macOS) or cmd/powershell (Windows).

## Drivers

If you have an Nvidia or AMD GPU, you may need to manually install drivers or other support packages for things to work well or at all.

### Nvidia

Run `nvidia-smi` on your system’s command line to verify that drivers and CUDA are installed. If this command fails, or doesn’t report versions, you will need to install drivers.

Go to the [CUDA Toolkit Downloads](https://developer.nvidia.com/cuda-downloads) and carefully follow the instructions for your system to get everything installed.

Confirm that `nvidia-smi` displays driver and CUDA versions after installation.

#### Linux - via Nvidia Container Runtime

An alternative to installing CUDA locally is to use the [Nvidia Container Runtime](https://developer.nvidia.com/container-runtime) to run the application in a container.

#### Windows - Nvidia cuDNN DLLs

An out-of-date cuDNN library can greatly hamper performance on 30-series and 40-series cards. Check with the community on discord to compare your `it/s` if you think you may need this fix.

1. Find your InvokeAI installation folder, e.g. `C:\Users\Username\InvokeAI\`.
2. Open the `.venv` folder, e.g. `C:\Users\Username\InvokeAI\.venv` (you may need to show hidden files to see it).
3. Navigate deeper to the `torch` package, e.g. `C:\Users\Username\InvokeAI\.venv\Lib\site-packages\torch`.
4. Copy the `lib` folder inside `torch` and back it up somewhere.

Next, download and copy the updated cuDNN DLLs:

1. Go to the [Cuda Docs](https://developer.nvidia.com/cudnn).
2. Create an account if needed and log in.
3. Choose the newest version of cuDNN that works with your GPU architecture. Consult the [cuDNN support matrix](https://docs.nvidia.com/deeplearning/cudnn/support-matrix/index.html) to determine the correct version for your GPU.
4. Download the latest version and extract it.
5. Find the `bin` folder, e.g. `cudnn-windows-x86_64-SOME_VERSION\bin`.
6. Copy and paste the `.dll` files into the `lib` folder you located earlier. Replace files when prompted.

If, after restarting the app, this doesn’t improve your performance, either restore your back up or re-run the installer to reset `torch` back to its original state.

### AMD

Run `rocm-smi` on your system’s command line verify that drivers and ROCm are installed. If this command fails, or doesn’t report versions, you will need to install them.

Go to the [ROCm Documentation](https://rocmdocs.amd.com/) and carefully follow the instructions for your system to get everything installed.

Confirm that `rocm-smi` displays driver and CUDA versions after installation.

#### Linux - via Docker Container

An alternative to installing ROCm locally is to use a [ROCm docker container](https://rocmdocs.amd.com/en/latest/Deep_learning/Deep_learning.html#docker-containers) to run the application in a container.