> This page is for MSA Search, version 2.3.0.
> For other versions, use one of these documentation indexes:
> - 2.6.0 (Latest) (default): https://docs.nvidia.com/nim/bionemo/msa-search/2.6.0/llms.txt
> - 2.5.0: https://docs.nvidia.com/nim/bionemo/msa-search/2.5.0/llms.txt
> - 2.4.0: https://docs.nvidia.com/nim/bionemo/msa-search/2.4.0/llms.txt
> - 2.3.0: https://docs.nvidia.com/nim/bionemo/msa-search/2.3.0/llms.txt
> - 2.2.0: https://docs.nvidia.com/nim/bionemo/msa-search/2.2.0/llms.txt
> - 2.1.0: https://docs.nvidia.com/nim/bionemo/msa-search/2.1.0/llms.txt
> - 2.0.0: https://docs.nvidia.com/nim/bionemo/msa-search/2.0.0/llms.txt
> - 1.0.0: https://docs.nvidia.com/nim/bionemo/msa-search/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 and Support Matrix for MSA Search NIM

## Sequence Databases and Search Types

The MSA Search NIM includes pre-indexed databases optimized for GPU Server operation:

| Database Name          | Use Case                      | Paired Search | Template Search | Source                                               |
|------------------------|-------------------------------|---------------|-----------------|------------------------------------------------------|
| uniref30_2302          | MSA and profile generation    | Yes           | Required        | [ColabFold Databases](https://colabfold.mmseqs.com/) |
| colabfold_envdb_202108 | MSA expansion                 | No            | No              | [ColabFold Databases](https://colabfold.mmseqs.com/) |
| pdb70_220313           | Template search (70% cluster) | No            | Yes             | [ColabFold Databases](https://colabfold.mmseqs.com/) |
| pdb100_230517          | Template search (100% cluster)| No            | Yes             | [ColabFold Databases](https://colabfold.mmseqs.com/) |
| pdb_20251028           | mmCIF structure retrieval     | No            | Yes             | [RCSB PDB](https://www.rcsb.org/)                    |

Total size: 1660 GB

<Note>
Paired MSA search requires databases with taxonomy information. Only uniref30_2302 supports paired search among the default databases.
</Note>

<Note>
Template search requires uniref30_2302 for profile generation plus at least one template database (PDB70 or PDB100). The profile generated from Uniref30 is used to search the template databases for structural homologs.
</Note>

<Note>
To reduce storage requirements, you can use task-specific database profiles that load only the databases needed for your use case. Refer to [Task-Specific Profiles and Custom Databases](/nim/bionemo/msa-search/2.3.0/task-specific-profiles-and-custom-databases) for details.
</Note>

## Supported Hardware

### Minimum System Hardware Requirements

| CPU Cores | RAM (GB) | NVMe SSD Storage (GB) | GPU Count                                              |
|-----------|----------|-----------------------|--------------------------------------------------------|
| 24        | 64       | 1660                  | One or more NVIDIA GPU from the Supported NVIDIA GPUs |

### Supported NVIDIA GPUs

The MSA Search NIM supports NVIDIA GPUs with at least 48 GB of GPU Memory.
It has been tested on the following recommended configurations:

| GPU                      | GPU Memory (GB) | GPU Count |
|--------------------------|-----------------|-----------|
| A100                     | 80              | 1-8       |
| H100                     | 80              | 1-8       |
| B200                     | 180             | 1-8       |
| L40S                     | 48              | 2-8       |
| RTX 6000 Ada Generation  | 48              | 2-4       |

<Note>
Starting with version 2.0.0, GPUs with 48GB memory (L40S, RTX 6000 Ada) require a minimum of 2 GPUs when using GPU Server (enabled by default). This is because GPU Server holds databases in memory for optimal performance.
</Note>


## Software Prerequisites

* Begin with Linux-based [Docker-supported operating system](https://docs.docker.com/engine/install/#supported-platforms)
* 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: 580
* 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 --rm --runtime=nvidia --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 H100 ...     On  | 00000000:1B:00.0 Off |                    0 |
| N/A   33C    P0             75W /  700W |       1MiB /  81559MiB |      0%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+

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

<Note>
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)
</Note>

Supported CPU architectures: x86_64 / amd64.

## NGC (NVIDIA GPU Cloud) Account

To download and run the MSA Search NIM, you need an NGC account and API key:

1. [Create an account on NGC](https://catalog.ngc.nvidia.com/) if you don't already have one
2. [Generate an API Key](https://org.ngc.nvidia.com/setup/api-key) - ensure that **NGC Catalog** is selected from the "Services Included" dropdown
3. Export the API key in your terminal:

```bash
export NGC_API_KEY=<your-api-key>
```

4. Log in to Docker with your NGC API key:

```bash
echo "$NGC_API_KEY" | docker login nvcr.io --username '$oauthtoken' --password-stdin
```

<Note>
Personal keys allow you to configure an expiration date, revoke, or delete the key using an action button, and rotate the key as needed. For more information about key types, refer to the [NGC User Guide](https://docs.nvidia.com/ngc/gpu-cloud/ngc-user-guide/index.html#ngc-api-keys).
</Note>