> This page is for MSA Search, version 1.0.0.
> 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.

# Support Matrix


## Sequence Databases and Search Types

The MSA NIM supports a variety of MMSeqs2-compatible databases (both those prepared by the user and pre-made indexes available as NGC Models).
The following databases are currently available on NGC:

| NGC Model Name       | Databases Supported                                         | Search Types Supported       | Size (GB) | Source                                                                                                  |
|------------------|-------------------------------------------------------------|------------------------------|-----------|---------------------------------------------------------------------------------------------------------|
| 0.1.8-colabfold  | Uniref30_2302, colabfold_envdb_202108, and PDB70_220313     | `alphafold2` and `colabfold` | 1200       | [ColabFold Databases](https://colabfold.mmseqs.com/)                                                    |
| 0.1.8-alphafold  | uniref90, small_bfd, and mgnify                             | `alphafold2`                 | 520      | [AlphaFold2 Databases](https://github.com/google-deepmind/alphafold/tree/main/scripts)                  |


## Supported Hardware


### Minimum System Hardware Requirements

| CPU Cores    | RAM (GB) | NVMe SSD Storage (GB) | GPU Count |
|--------|-----------------|-----------|-------|
| 24  | 64              | 1300       | One or More NVIDIA GPU from the list of 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 |
| L40s   | 48              | 1-8 |
| RTX 6000 Ada Generation (beta) | 48 | 1-4 |


## Testing Locally Available Hardware

* Begin with [Docker-supported operating system](https://docs.docker.com/engine/faq/#does-docker-run-on-linux-macos-and-windows)
* 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 525.78.01    Driver Version: 525.78.01    CUDA Version: 12.0     |
>|-------------------------------+----------------------+----------------------+
>| 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      |
>|=============================================================================|
>+-----------------------------------------------------------------------------+
>```

<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>