> This page is for Boltz-2, version 1.2.0.
> For other versions, use one of these documentation indexes:
> - 1.10.0 (Latest) (default): https://docs.nvidia.com/nim/bionemo/boltz2/1.10.0/llms.txt
> - 1.9.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.9.0/llms.txt
> - 1.8.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.8.0/llms.txt
> - 1.7.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.7.0/llms.txt
> - 1.6.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.6.0/llms.txt
> - 1.5.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.5.0/llms.txt
> - 1.4.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.4.0/llms.txt
> - 1.3.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.3.0/llms.txt
> - 1.2.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.2.0/llms.txt
> - 1.1.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.1.0/llms.txt
> - 1.0.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.0.0/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

## September 2, 2025

## Release 1.2.0

### Summary

This release introduces support for longer sequence processing and additional GPU compatibility, along with performance optimizations.

### Key Features

* Support longer sequence (2048 length) processing for TRT engines on A100, H100, and B200 with memory larger than 48GB
* Support PyTorch and TRT engines with NVIDIA RTX6000 (RTX6000 and RTX6000-Ada GPU Workstation Edition GPU)
* Improve model execution speed
* Optimize memory usage during inference

### Notes and Limitations

* Ensure you use this NIM with GPUs with at least 48 GB of VRAM.
* **TensorRT Engine Speedup**: To achieve optimal prediction speedup using TRT engines, this version utilizes TensorRT 10.11.0. Refer to the [TensorRT 10.11.0 Support Matrix](https://docs.nvidia.com/deeplearning/tensorrt/10.11.0/getting-started/support-matrix.html) to ensure your CUDA Driver and CUDA Toolkit versions are compatible.

  **CUDA Requirements for TensorRT 10.11.0**:

  * **CUDA Driver**: 12.9, 12.8 update 1, 12.6 update 3, 12.5 update 1, 12.4 update 1, 12.3 update 2, 12.2 update 2, 12.1 update 1, 12.0 update 1, 11.8, 11.7 update 1, 11.6 update 2, 11.5 update 2, 11.4 update 4, 11.3 update 1, 11.2 update 2, 11.1 update 1, 11.0 update 3
  * **CUDA Toolkit**: Must be compatible with the chosen CUDA Driver version
  * **Platform Support**: Linux x86-64, Windows x64, Linux SBSA, and NVIDIA JetPack

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

## August 1, 2025

## Release 1.1.0

### Summary

Boltz-2 enables accurate biomolecular structure
prediction from input sequences including proteins, DNA, RNA, and ligands. It also supports constrained guidance for complex structural prediction using bond and pocket specifications.

This release adds binding affinity prediction for ligands and complexes. The underlying Boltz-2 model has been updated to version 2.2.0. More of the Boltz-2 scores are included in the output. There are minor bug fixes to improve the overall user experience.

### Notes and Limitations

Ensure you use this NIM with GPUs with at least 48 GB of VRAM.

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

## June 10, 2025

## Release 1.0.0

### Summary

This is the first release of the Boltz-2 NIM. Boltz-2 enables accurate biomolecular structure
prediction from input sequences including proteins, DNA, RNA, and ligands. It also supports constrained guidance for complex structural prediction using bond and pocket specifications.

### Notes and Limitations

Ensure you use this NIM with GPUs with at least 48 GB of VRAM.

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