> This page is for AlphaFold2-Multimer, version 1.0.0 (Latest) (default).
> For other versions, use one of these documentation indexes:
> - 1.0.0 (Latest) (default): https://docs.nvidia.com/nim/bionemo/alphafold2-multimer/1.0.0/llms.txt

> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nim/bionemo/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nim/bionemo/_mcp/server.

# Prerequisites

* Begin with [Docker Engine](https://docs.docker.com/engine/).
* Install [Docker](https://docs.docker.com/engine/install/) (minimum version: `23.0.1`).
* Install [NVIDIA Drivers](https://www.nvidia.com/en-us/drivers/) (minimum version: `535`).
* Install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installing-the-nvidia-container-toolkit) (minimum version: `1.13.5`).
* Verify your container runtime supports NVIDIA GPUs by running:

```bash
docker run -it --rm --runtime=nvidia --gpus all ubuntu nvidia-smi
```

Example output:

```bash
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.78.01    Driver Version: 525.78.01    CUDA Version: 12.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA GeForce ...  Off  | 00000000:01:00.0 Off |                  N/A |
| 41%   30C    P8     1W / 260W |   2244MiB / 11264MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
+-----------------------------------------------------------------------------+
```

<Note>
For more information on enumerating multi-GPU systems, refer to the NVIDIA Container Toolkit [GPU enumeration](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/docker-specialized.html#gpu-enumeration) topic.
</Note>

## Supported Hardware

These configurations are the supported hardware for this NIM. Runtime expectations are in [Performance](/nim/bionemo/alphafold2-multimer/performance).

The AlphaFold2-Multimer NIM is configured to run on a single GPU. The minimum GPU memory requirement for the AlphaFold2-Multimer NIM is 32 GB. The AlphaFold2-Multimer NIM should run on any NVIDIA GPU that meets this minimum hardware requirement and has compute capability 8.0 or higher. The AlphaFold2-Multimer NIM also requires at least 1300 GB of free SSD space to store the MSA databases used by the NIM endpoints.

In summary, users looking to successfully run the AlphaFold2-Multimer NIM for short sequences and multimers should have a system with:

* One NVIDIA GPU with at least 32 GB of VRAM and Compute Capability 8.0 or higher.
* At least 64 GB of RAM.
* A CPU with at least 24 available cores.
* At least 512 GB of free SSD drive space.

For optimal performance on long sequences and multimers and multiple MSA databases, use a system with:

* At least one NVIDIA GPU with 80 GB of VRAM (for example, A100 80GB).
* At least 128 GB of RAM.
* A CPU with at least 36 available cores.
* At least 512 GB of free fast NVMe SSD drive space.

## NGC Account

1. [Create an account on NGC](https://catalog.ngc.nvidia.com/).
2. [Generate an API Key](https://org.ngc.nvidia.com/setup/api-key).
3. Log in to Docker with your NGC API key: `docker login nvcr.io --username='$oauthtoken' --password=<your personal NGC key>`

## NGC CLI Tool

1. Download the [NGC CLI tool](https://org.ngc.nvidia.com/setup/installers/cli) for your OS.

<Warning>
Use NGC CLI version `3.41.1` or newer. Here is the command to install this on AMD64 Linux in your home directory:
</Warning>

```bash
wget --content-disposition https://api.ngc.nvidia.com/v2/resources/nvidia/ngc-apps/ngc_cli/versions/3.41.3/files/ngccli_linux.zip -O ~/ngccli_linux.zip && \
unzip ~/ngccli_linux.zip -d ~/ngc && \
chmod u+x ~/ngc/ngc-cli/ngc && \
echo "export PATH=\"\$PATH:~/ngc/ngc-cli\"" >> ~/.bash_profile && source ~/.bash_profile
```

2. Set up your NGC CLI tool locally. You will need your API key for this.

```bash
ngc config set
```

<Note>
After you enter your API key, you may see multiple options for the org and team. Select as desired or hit enter to accept the default.
</Note>

3. Log in to NGC.

You will need to log in to NGC with Docker and set the `NGC_API_KEY` environment variable to pull images:

```bash
docker login nvcr.io
Username: $oauthtoken
Password: <Enter your NGC key here>
```

Then, set the relevant environment variables in your shell. You will need to set the `NGC_CLI_API_KEY` variable:

```bash
export NGC_CLI_API_KEY=<Enter your NGC key here>
```

4. Set up your NIM (model) cache.

The NIM cache allows you to download models and store previously downloaded models so that you do not need to download
them again later when you run the NIM again. The NIM cache must be readable and writable by the NIM, so in addition
to creating the directory, the permissions on this directory need to be set to globally readable and writable. The
NIM cache directory can be set up as follows:

```bash
## Create the NIM cache directory.
mkdir -p ~/.cache/nim

## Set the NIM cache directory permissions to 777.
chmod -R 777 ~/.cache/nim
```

```bash
## If you hit permissions issues after running the NIM and downloading the model for AlphaFold2,
## set model & database permissions to 777 as well. Required for running the NIM!
sudo chmod -R 777 ~/.cache/nim/alphafold2-data_v1.1.0
```


<Warning>
If you experience issues downloading the model via running the NIM, such as deploying in an ["air-gapped" environment](/nim/bionemo/alphafold2-multimer/configuring-the-nim-at-runtime#air-gapped-nim-deployment), you can manually download the AlphaFold2 model using the following command: `ngc registry model download-version nim/deepmind/alphafold2-data:1.1.0` followed by `sudo chmod -R 777 ~/.cache/nim/alphafold2-data_v1.1.0`
</Warning>


Throughout this documentation, the `$LOCAL_NIM_CACHE` environment variable is assumed to use the local `~/.cache/nim` directory. You can [set the model cache path to any directory](/nim/bionemo/alphafold2-multimer/configuring-the-nim-at-runtime#change-the-nim-cache-location). NVIDIA recommends using a high-speed SSD for fast read and write access to the AF2 model and databases. Run the `sudo chmod -R 777 $LOCAL_NIM_CACHE` command to avoid file permission issues with the NIM container, regardless of the specified directory.

Now, you should be able to pull the container and download the model using the environment variables.
To get started, refer to the [Quickstart Guide](/nim/bionemo/alphafold2-multimer/quickstart-guide).