> This page is for NVIDIA NIM for Batched Geometry Relaxation, version 1.1.0 (Latest) (default).
> For other versions, use one of these documentation indexes:
> - 1.1.0 (Latest) (default): https://docs.nvidia.com/nim/alchemi/alchemi-bgr/1.1.0/llms.txt
> - 1.0.0: https://docs.nvidia.com/nim/alchemi/alchemi-bgr/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/alchemi/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nim/alchemi/_mcp/server.

# Prerequisites

Review the following system requirements and setup instructions before deploying
NIM for BGR.

## System Requirements

- **NVIDIA AI Enterprise License**: NVIDIA NIM for BGR is available for self-hosting under the NVIDIA AI Enterprise (NVAIE) License.
- **NVIDIA GPUs**: NIM for BGR runs on single or multiple GPUs. Minimum GPU memory is 8 GB. The system requires [CUDA compute capability](https://developer.nvidia.com/cuda-gpus) 8.0 or higher.
- **CPU**: x86 processor (modern processor recommended).
- **Storage**: 15 GB of disk space for the Docker container.
- **Operating System**: A Linux distribution that meets the following criteria:
  - Is [supported by the NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/supported-platforms.html).
  - Has `glibc` 2.35 or higher (refer to the output of `ld -v`).
  - NVIDIA recommends Ubuntu 20.04 or later.
- **CUDA Drivers**: Follow the [installation guide](https://docs.nvidia.com/cuda/cuda-installation-guide-linux). NVIDIA recommends:
  - Using a network repository as part of a [package manager installation](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/#package-manager-installation) and skipping the CUDA toolkit installation, because libraries are available within the NIM container.
  - Installing the [open kernels](https://github.com/NVIDIA/open-gpu-kernel-modules) for your driver version.

Refer to the [Frameworks Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html)
for NVIDIA driver version compatibility. Ensure that the latest compatible
NVIDIA driver is installed before you launch NIM containers.

## Docker Setup

Do the following to set up Docker:

1. Install [Docker](https://docs.docker.com/engine/install/).
2. Install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installing-the-nvidia-container-toolkit).
3. After installing the toolkit, follow the
   [Configure Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#configuring-docker)
   instructions.
4. To verify your setup, run the following command:

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

   This confirms the CUDA driver version and available GPUs.

   <Accordion title="Example output">

   ```bash
   +-----------------------------------------------------------------------------------------+
   | NVIDIA-SMI 550.54.14              Driver Version: 550.54.14      CUDA Version: 12.4     |
   |-----------------------------------------+------------------------+----------------------+
   | 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 80GB HBM3          On  |   00000000:1B:00.0 Off |                    0 |
   | N/A   36C    P0            112W /  700W |   78489MiB /  81559MiB |      0%      Default |
   |                                         |                        |             Disabled |
   +-----------------------------------------+------------------------+----------------------+

   +-----------------------------------------------------------------------------------------+
   | Processes:                                                                              |
   |  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
   |        ID   ID                                                               Usage      |
   |=========================================================================================|
   |  No running processes found                                                             |
   +-----------------------------------------------------------------------------------------+
   ```

   </Accordion>

## NGC Account

To create an NVIDIA GPU Cloud (NGC) account and authenticate requests, do the
following:

1. [Create an account on NGC](https://ngc.nvidia.com/signin).
2. [Generate an API Key](https://org.ngc.nvidia.com/setup/api-key).
3. Download the [NGC CLI tool](https://org.ngc.nvidia.com/setup/installers/cli) for your operating system.
4. Set up the NGC CLI tool locally by running the following command in your terminal:

   ```bash
   ngc config set
   ```

5. Log in to NGC using Docker to pull images:

   ```bash
   docker login nvcr.io
   ```

   ```bash
   Username: $oauthtoken
   Password: <Enter your NGC key here>
   ```

6. Set the relevant environment variables in your shell, including
   `NGC_API_KEY`:

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

## Model Setup

For supported models, sourcing requirements, and mounting instructions, refer
to [Supported Models](/nim/alchemi/alchemi-bgr/overview#supported-models). For download hints and
container launch arguments by model type (MACE, AIMNet2, TensorNet), refer to
[Custom Models](/nim/alchemi/alchemi-bgr/custom-models).