> This page is for version v4.8.0 (default).
> For other versions, use one of these documentation indexes:
> - v4.8.0 (default): https://docs.nvidia.com/clara/parabricks/v4.8.0/llms.txt
> - v4.7.1: https://docs.nvidia.com/clara/parabricks/v4.7.1/llms.txt

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

# Installation Requirements

Before you begin running NVIDIA Parabricks, ensure the following hardware and software requirements are met.

## Hardware Requirements

The following are hardware requirements for running Parabricks:

* Any NVIDIA GPU with at least 16 GB of GPU RAM that supports one of the following CUDA architectures: 75, 80, 86, 89, 90, 100, 103, 107, 120, or 121. NVIDIA Parabricks has been tested on the following NVIDIA GPUs:

  * T4
  * A10, A30, A40, A100, A6000
  * L4, L40
  * H100, H200
  * GH200
  * B200, B300
  * GB200, GB300, DGX Station with GB300
  * RTX PRO 6000 Blackwell Server Edition, RTX PRO 4500 Blackwell
  * NVIDIA GB10 (DGX Spark)
  * NVIDIA Rubin GPUs

* All tools require at least 16 GB of GPU memory per GPU. The following tools have defaults that require more
  than 16 GB but have options to lower the device memory usage to fit on devices with 16 GB of GPU memory.

  * [**haplotypecaller**](/clara/parabricks/v4.8.0/tool-reference/tools/haplotypecaller) and [**mutectcaller**](/clara/parabricks/v4.8.0/tool-reference/tools/mutectcaller): Default configurations require at least 18 GB
    of GPU memory per GPU. To use a 16 GB GPU, use the `--htvc-low-memory` and `--mutect-low-memory` options, respectively.
  * [**minimap2**](/clara/parabricks/v4.8.0/tool-reference/tools/minimap2): Default configuration requires at least 18 GB
    of GPU memory per GPU. To use a 16 GB GPU, use the `--low-memory` option.
  * [**rna_fq2bam**](/clara/parabricks/v4.8.0/tool-reference/tools/rna_fq2bam): Default configuration requires at least 24 GB of GPU memory per GPU. To use a GPU with
    less device memory, use the `--low-memory` option.

* System Requirements:

  * A 2 GPU system should have at least 100GB CPU RAM and at least 24 CPU threads.
  * A 4 GPU system should have at least 196GB CPU RAM and at least 32 CPU threads.
  * A 8 GPU system should have at least 392GB CPU RAM and at least 48 CPU threads.

* Because of its large pangenome index files, [**giraffe**](/clara/parabricks/v4.8.0/tool-reference/tools/giraffe) (and the
  [**pangenome_germline**](/clara/parabricks/v4.8.0/tool-reference/tools/pangenome_germline) pipeline that uses it) can require more CPU RAM and CPU threads
  than listed above. Refer to [System Requirements and Useful Options for Performance](/clara/parabricks/v4.8.0/tool-reference/tools/giraffe#system-requirements-and-useful-options-for-performance)
  for details.

> **Note**
>
> * Running Parabricks on a single GPU is supported but not recommended. If using a single GPU, ensure that your system meets the 2-GPU requirement mentioned above.
> * Parabricks is supported on time-sliced virtual (vGPU) but not on Multi-Instance (MIG) GPUs.

## Software Requirements

The following are software requirements for running Parabricks:

* Any NVIDIA CUDA driver that is compatible with CUDA 13.3.X (535+, 570+, 580+, 590+, 595+, 610+, or similar). The CUDA Driver forward compatibility documentation for CUDA 13.3 can be found at [https://docs.nvidia.com/deploy/cuda-compatibility/610/forward-compatibility.html](https://docs.nvidia.com/deploy/cuda-compatibility/610/forward-compatibility.html). Consumer (i.e., not workstation or data center GPUs) will need a driver with version 610+ or newer.
* Any Linux operating system that supports the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/index.html)
* Docker version 20.10 (or higher).

Refer to the following pages for more information on supported driver configurations:

* [CUDA Toolkit, Driver and Architecture Matrix](https://docs.nvidia.com/datacenter/tesla/drivers/latest/cuda-toolkit-driver-and-architecture-matrix.html)
* [CUDA Compatibility](https://docs.nvidia.com/deploy/cuda-compatibility/)

> **Note**
>
> Parabricks is available as Docker image. For Singularity users, refer to
> [Singularity and Docker](https://docs.sylabs.io/guides/2.6/user-guide/singularity_and_docker.html)
> for more information about importing a Docker image into a Singularity Image,

## Verify Hardware and Software Requirements

Ensure you verify the following hardware and software requirements before running Parabricks.

### Checking for Available NVIDIA Hardware and Driver

To check your NVIDIA hardware and driver version, use the `nvidia-smi` command:

```sh
$ nvidia-smi
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 615.71.09              KMD Version: 615.71.09     CUDA UMD Version: 13.4     |
+-----------------------------------------+------------------------+----------------------+
| 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 RTX PRO 6000 Blac...    On  |   00000000:1A:00.0 Off |                  Off |
| 30%   30C    P8              9W /  600W |     127MiB /  97887MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|    0   N/A  N/A            4445      G   /usr/lib/xorg/Xorg                       76MiB |
|    0   N/A  N/A            4552      G   /usr/bin/gnome-shell                     22MiB |
+-----------------------------------------------------------------------------------------+
```

This shows the following important information:

* The NVIDIA driver version is 615.71.09.
* The supported CUDA driver API is 13.4.
* The GPU has 96 GB of memory.

### Checking for Available CPU RAM and Threads

To check how much RAM and CPU threads in your machine, run the following:

```sh
# To check available memory
$ cat /proc/meminfo | grep MemTotal

# To check available number of threads
$ cat /proc/cpuinfo | grep processor | wc -l
```

### Checking for NVIDIA Container Toolkit Installation

To make sure you have the NVIDIA Container Toolkit installed, run this command:

```sh
$ docker run --rm --gpus all nvidia/cuda:13.3.1-base-ubuntu22.04 nvidia-smi
```

When it finishes downloading the container, it will run the `nvidia-smi` command and show you the same output as above.

### Checking for Python Version

To get the version of Python you have, enter the following command:

```sh
$ python3 --version
```

Make sure it is at least version 3 (for example, 3.6.9 or 3.7).