> This page is for MSA Search, version 2.0.0.
> 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 ColabFold databases optimized for GPU Server operation:
| Database Name | Search Types Supported | Source |
|------------------------|------------------------------|------------------------------------------------------|
| Uniref30_2302 | `alphafold2` and `colabfold` | [ColabFold Databases](https://colabfold.mmseqs.com/) |
| colabfold_envdb_202108 | `alphafold2` and `colabfold` | [ColabFold Databases](https://colabfold.mmseqs.com/) |
| PDB70_220313 | `alphafold2` and `colabfold` | [ColabFold Databases](https://colabfold.mmseqs.com/) |
Total size: 1400 GB
## Supported Hardware
### Minimum System Hardware Requirements
| CPU Cores | RAM (GB) | NVMe SSD Storage (GB) | GPU Count |
|-----------|----------|-----------------------|--------------------------------------------------------|
| 24 | 64 | 1400 | 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 | 192 | 1-8 |
| L40S | 48 | 2-8 |
| RTX 6000 Ada Generation | 48 | 2-4 |
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.
## 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: 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 --rm --runtime=nvidia --gpus all ubuntu nvidia-smi
```
Example output:
```bash
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 |
|-------------------------------+----------------------+----------------------+
| 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 |
+-----------------------------------------------------------------------------+
```
For more information on enumerating multi-GPU systems, see 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)
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=
```
4. Log in to Docker with your NGC API key:
```bash
echo "$NGC_API_KEY" | docker login nvcr.io --username '$oauthtoken' --password-stdin
```
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).