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
colabfold_envdb_202108	`alphafold2` and `colabfold`	ColabFold Databases
PDB70_220313	`alphafold2` and `colabfold`	ColabFold Databases

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

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.

Software Prerequisites#

Begin with Linux-based Docker-supported operating system
Install Docker - minimum version: 23.0.1
Install NVIDIA Drivers - minimum version: 535
Install the NVIDIA Container Toolkit - minimum version: 1.13.5
Verify your container runtime supports NVIDIA GPUs by running

docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi

Example output:

+-----------------------------------------------------------------------------+
| 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                                                 |
+-----------------------------------------------------------------------------+

Note

For more information on enumerating multi-GPU systems, see the NVIDIA Container Toolkit’s GPU Enumeration Docs

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:

Create an account on NGC if you don’t already have one
Generate an API Key - ensure that NGC Catalog is selected from the “Services Included” dropdown
Export the API key in your terminal:

export NGC_API_KEY=<your-api-key>

Log in to Docker with your NGC API key:

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.