> 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.

# Release Notes

## November 24, 2025

## Release 2.0.0

### Summary

This release represents a major update to the MSA Search NIM, featuring significant performance improvements and streamlined database management. The GPU Server is now enabled by default, providing better out-of-the-box performance with the included ColabFold databases. This release also includes an upgrade to MMSeqs2 version 18, enhanced stability, and expanded hardware support.

### Key Changes

#### GPU Server Enabled by Default

The GPU Server is now enabled by default, improving performance for the default ColabFold databases. The previous release allows enabling the GPU Server required manual configuration and custom database indices created with the `makepaddedseqdb` command. This release includes the following features:

* **Reduced latency**: The GPU Server intelligently stores and loads target sequence databases in GPU memory, reducing query latency
* **Simplified deployment**: No additional configuration or separate database indexing steps needed to achieve optimal performance with default databases
* **Pre-indexed databases**: The included ColabFold databases (Uniref30_2302, colabfold_envdb_202108, PDB70_220313) come pre-indexed and optimized for GPU Server use
* **Better resource utilization**: Efficient GPU memory management enables faster search and alignment operations

#### Database Changes

With the GPU Server now optimized and enabled by default, this release focuses on the ColabFold databases for optimal performance:

* **Default databases**: ColabFold databases (Uniref30_2302, colabfold_envdb_202108, PDB70_220313) provide better performance and are recommended for most use cases
* **AlphaFold2 databases**: The CPU-only AlphaFold2 databases (uniref90, small_bfd, mgnify) are not included for GPU Server operation. However, both `alphafold2` (iterative) and `colabfold` (cascaded) search types remain fully supported when used with the provided Colabfold databases.
* **External database support**: Users can still use custom or external databases by mounting them via the `MODEL_PATH` environment variable

#### MMSeqs2 Version Upgrade

This release upgrades to MMSeqs2 version 18, bringing:

* Enhanced performance optimizations
* Better stability and error handling

#### Hardware Support

Added support for NVIDIA B200 GPUs, expanding deployment options for the latest NVIDIA hardware.

### New Features and Improvements

* GPU Server enabled by default for optimal performance with ColabFold databases
* Upgraded to MMSeqs2 version 18 with improved accuracy and performance
* Enhanced stability and error handling throughout the pipeline
* Added NVIDIA B200 GPU support
* Optimized database loading and GPU memory management

### Performance Enhancements

* Reduced query latency through default GPU Server activation
* Faster database loading with pre-indexed ColabFold databases
* Better throughput scaling across multiple GPUs

### Migration Notes

#### Configuration Changes

* **GPU Server**: GPU Server is now enabled by default; to disable it, set `NIM_DISABLE_GPU_SERVER=True`
* **MSA Depth**: With GPU Server enabled by default, the maximum MSA depth must be set globally for all requests through the `NIM_GLOBAL_MAX_MSA_DEPTH` environment variable at container startup for performance reasons. This parameter cannot be changed per-request. If you require a specific MSA depth, configure this environment variable accordingly.

#### Hardware Requirements

* **48GB GPUs**: For 48GB GPUs (L40S, RTX 6000 Ada), the minimum requirement is now 2 GPUs when using GPU Server (default). This is because GPU Server holds databases in memory for optimal performance.

#### For Existing Users

No migration steps are required. The default ColabFold databases will automatically benefit from the enabled GPU Server, providing improved performance.

> **Note**
>
> The `alphafold2` search type (iterative search) is still fully supported when using appropriate databases like the included ColabFold databases.

## March 17, 2025

## Release 1.0.0

### Summary

This is the first release of MSA Search NIM. MSA Search enables accurate protein structure
prediction from an input protein sequence by predicting potential structural similarities
with previously-observed proteins.

This NIM is not a deep learning model but makes use of NVIDIA GPUs and software libraries for
accelerated multiple sequence alignment. The NIM relies on GPU-accelerated MMSeqs2 to provide
fast, accurate sequence database search and multiple sequence alignment.

* Supported search_types: `alphafold2` and `colabfold`. **Note**: Different search types require different database types.
* Supported NVIDIA GPUs: At least 48GB of GPU Memory (A100, H100, and L40s). Currently there is no official support for GPUs with less than 48 GB of GPU Memory.

### Model Variants

* ColabFold, which contains the databases Uniref30_2302, colabfold_envdb_202108, and PDB70_220313.
* AlphaFold, which contains the databases uniref90, mgnify, and smallbfd, as used in AlphaFold2.

### Notes and Limitations

Ensure you use this NIM with GPUs with at least 48 GB of VRAM. In addition, this NIM requires
roughly 1.3 Terabytes (1300 Gigabytes) of fast NVMe SSD storage to store the databases.

**Note**: While there are many options for tuning this NIM's performance, for most users the defaults will
provide a balanced performance experience.