> This page is for OpenFold3, version 1.1.0. > For other versions, use one of these documentation indexes: > - 1.6.0 (Latest) (default): https://docs.nvidia.com/nim/bionemo/openfold3/1.6.0/llms.txt > - 1.5.0: https://docs.nvidia.com/nim/bionemo/openfold3/1.5.0/llms.txt > - 1.4.0: https://docs.nvidia.com/nim/bionemo/openfold3/1.4.0/llms.txt > - 1.3.0: https://docs.nvidia.com/nim/bionemo/openfold3/1.3.0/llms.txt > - 1.2.0: https://docs.nvidia.com/nim/bionemo/openfold3/1.2.0/llms.txt > - 1.1.0: https://docs.nvidia.com/nim/bionemo/openfold3/1.1.0/llms.txt > - 1.0.0: https://docs.nvidia.com/nim/bionemo/openfold3/1.0.0/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 Before you begin using the OpenFold3 NIM, ensure the following requirements described on this page are met. * Begin with a [Linux Distribution that supports NVIDIA Driver >=580 ](https://docs.nvidia.com/datacenter/tesla/driver-installation-guide/introduction.html#supported-linux-distributions) * The OpenFold3 NIM runs CUDA 13.0, which requires NVIDIA Driver >=580, see [compatibility matrix](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html#id5) * We have confirmed the installation and setup workflow below, on systems (a) with Ubuntu 22.04, (b) without NVSwitch. For systems with NVSwitch, [fabricmanager may be needed](https://docs.nvidia.com/dgx/dgx-os-7-user-guide/installing_on_ubuntu.html#installing-the-gpu-driver). * Install [NVIDIA Drivers](https://www.nvidia.com/en-us/drivers/) - minimum version: 580 * Install [Docker](https://docs.docker.com/engine/install/) - minimum version: 23.0.1 * 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 ## Known issues * There are known issues with [NVIDIA Driver 580.95.05 on Hopper GPUs subrevision 3](https://docs.nvidia.com/datacenter/tesla/tesla-release-notes-580-95-05/index.html#known-issues). ## Installing NVIDIA Drivers To install NVIDIA drivers, you can use either your local or remote machine. ### Option 1: Interactive Installation (Local Machine) Visit the [NVIDIA Drivers download page](https://www.nvidia.com/en-us/drivers/) and use the dropdown menu to select your GPU and operating system to download the appropriate driver. ### Option 2: Command Line Installation (Remote Machine/SSH) If you're working on a remote machine over SSH, you can download and install the driver using command line tools: 1. Determine your GPU model and OS version: ```bash # Check GPU model lspci | grep -i nvidia # Example output: # 00:1e.0 3D controller: NVIDIA Corporation GH100 [H100 PCIe] (rev a1) # Check OS version cat /etc/os-release # Example output for Ubuntu: # NAME="Ubuntu" # VERSION="22.04.3 LTS (Jammy Jellyfish)" # ID=ubuntu # VERSION_ID="22.04" ``` 2. Find the driver 580+ download link: a. On your **local machine** (with browser), visit the [NVIDIA Drivers download page](https://www.nvidia.com/en-us/drivers/) b. Select your GPU information: - **Product Type**: Tesla (for datacenter GPUs like H100, H200, B200, A100, L40S) or GeForce (for consumer GPUs) - **Product Series**: H100, H200, B200, A100, L40S, or your specific GPU series - **Operating System**: Linux 64-bit - **Download Type**: Production Branch - **Language**: English (US) c. Click **Search** to find driver version 580 or higher d. On the results page, **right-click** the **Download** button and select **Copy Link Address** e. The link will look like: ``` https://us.download.nvidia.com/tesla/580.95.05/NVIDIA-Linux-x86_64-580.95.05.run ``` or for repository installation: ``` https://us.download.nvidia.com/tesla/580.95.05/nvidia-driver-local-repo-ubuntu2404-580.95.05_1.0-1_amd64.deb ``` For **Ubuntu/Debian**, use the `.deb` package. For **RHEL/CentOS**, use the `.rpm` package. For other distributions, use the `.run` installer. 3. Direct driver URLs for common configurations: **For Ubuntu 24.04 (Noble):** ```bash # Driver 580.95.05 for H100/H200/B200/A100/L40S https://us.download.nvidia.com/tesla/580.95.05/nvidia-driver-local-repo-ubuntu2404-580.95.05_1.0-1_amd64.deb ``` **For Ubuntu 22.04 (Jammy):** ```bash # Driver 580.95.05 for H100/H200/B200/A100/L40S https://us.download.nvidia.com/tesla/580.95.05/nvidia-driver-local-repo-ubuntu2204-580.95.05_1.0-1_amd64.deb ``` **For RHEL 8/Rocky Linux 8:** ```bash # Driver 580.95.05 for H100/H200/B200/A100/L40S https://us.download.nvidia.com/tesla/580.95.05/nvidia-driver-local-repo-rhel8-580.95.05-1.0-1.x86_64.rpm ``` Always check the [NVIDIA Driver Downloads](https://www.nvidia.com/Download/index.aspx) page for the latest driver version compatible with your GPU and OS. The following commands are for **Ubuntu 24.04**. If you're using Ubuntu 22.04 or other versions, replace `ubuntu2404` in the URLs and paths with your version (e.g., `ubuntu2204` for Ubuntu 22.04). 4. Use `wget` to download the driver on your remote machine: ```bash # Example for Ubuntu 24.04 with driver version 580.95.05 wget https://us.download.nvidia.com/tesla/580.95.05/nvidia-driver-local-repo-ubuntu2404-580.95.05_1.0-1_amd64.deb ``` Replace the URL with the appropriate driver version and distribution for your system. Use the URL you copied from step 2 or select from the common configurations listed above. 5. Install the local repository using `dpkg` (for Ubuntu/Debian): ```bash sudo dpkg -i nvidia-driver-local-repo-ubuntu2404-580.95.05_1.0-1_amd64.deb ``` For **RHEL/CentOS/Rocky Linux**: ```bash sudo rpm -i nvidia-driver-local-repo-rhel8-580.95.05-1.0-1.x86_64.rpm ``` 6. Update package lists and install the driver: For **Ubuntu/Debian**: ```bash # Copy the GPG key sudo cp /var/nvidia-driver-local-repo-ubuntu2404-580.95.05/nvidia-driver-local-*-keyring.gpg /usr/share/keyrings/ # Update package cache sudo apt-get update # Install the driver sudo apt-get install -y cuda-drivers ``` For **RHEL/CentOS/Rocky Linux**: ```bash # Update package cache sudo dnf clean all sudo dnf makecache # Install the driver sudo dnf install -y cuda-drivers ``` 7. After installation, reboot to load the new driver: ```bash sudo reboot ``` 8. After reboot, verify the driver is installed correctly: ```bash nvidia-smi ``` You should see output showing your GPU(s) and driver version 580 or higher with CUDA version 13.0 or higher. Example expected output: ``` +-----------------------------------------------------------------------------+ | NVIDIA-SMI 580.95.05 Driver Version: 580.95.05 CUDA Version: 13.0 | |-------------------------------+----------------------+----------------------+ | 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 H100 PCIe Off | 00001E:00:00.0 Off | 0 | | N/A 30C P0 68W / 350W | 0MiB / 81559MiB | 0% Default | | | | Disabled | +-------------------------------+----------------------+----------------------+ ``` ### Troubleshooting Driver Installation **Driver version mismatch**: If `nvidia-smi` shows an older driver version, ensure you've rebooted after installation. **CUDA version**: The driver must support CUDA 13.0 or higher. Check the CUDA version in the `nvidia-smi` output. **Secure Boot**: If you have Secure Boot enabled, you may need to sign the NVIDIA kernel modules or disable Secure Boot in your BIOS. **Previous driver versions**: If you have older NVIDIA drivers installed, you may need to remove them first: ```bash sudo apt-get remove --purge nvidia-* sudo apt-get autoremove ``` ## Verifying GPU Access Verify your container runtime supports NVIDIA GPUs by running: ```bash docker run --rm --gpus all ubuntu nvidia-smi ``` >Example output: ```bash +-----------------------------------------------------------------------------+ | NVIDIA-SMI 580.82.07 Driver Version: 580.82.07 CUDA Version: 13.0 | |-------------------------------+----------------------+----------------------+ | 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 | |=============================================================================| +-----------------------------------------------------------------------------+ ``` For more information on enumerating multi-GPU systems, refer to the NVIDIA Container Toolkit’s [GPU Enumeration Docs](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/docker-specialized.html?highlight=enumerate#gpu-enumeration) ## NGC (NVIDIA GPU Cloud) 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. Docker log in with your NGC API key using ``docker login nvcr.io --username='$oauthtoken' --password=${NGC_API_KEY}`` ## NGC CLI Tool 1. Download the [NGC CLI tool](https://org.ngc.nvidia.com/setup/installers/cli) for your OS. Use NGC CLI version ``3.41.1`` or newer. Here is the command to install this on AMD64 Linux in your home directory: ```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’ll need your API key for this!): ```bash ngc config set ``` 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. 3. Log in to NGC You'll need log in to NGC via Docker and set the NGC_API_KEY environment variable to pull images: ```bash docker login nvcr.io Username: $oauthtoken Password: ``` Then, set the relevant environment variables in your shell. You will need to set the `NGC_API_KEY` variable: ```bash export NGC_API_KEY= ``` 4. Set up your NIM cache The NIM needs a directory on your system called the NIM cache, where it can - (a) download the model artifact (checkpoints and TRT engines) - (b) read the model artifact if it has been previously downloaded The NIM cache directory must - (i) reside on a disk with at least 15GB of storage - (ii) have a permission state that allows the NIM to read, write, an execute The NIM cache directory can be set up as follows, if your home directory '~' is on a disk with enough storage ```bash ## Create the NIM cache directory in a location with sufficent storage mkdir -p ~/.cache/nim ## Set the NIM cache directory permissions to allow all (a) users to read, write, and execute (rwx) sudo chmod -R a+rwx ~/.cache/nim ``` Now, you should be able to pull the container and download the model using the environment variables. To get started, see the quickstart guide.