> This page is for Boltz-2, version 1.6.0.
> 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

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> For a complete documentation index, see https://docs.nvidia.com/nim/bionemo/llms.txt.
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## Release 1.3.0

### Summary

This release adds support for GB200 GPU with ARM architecture and extends sequence processing capabilities for both PyTorch and TensorRT backends.

### Key Features

* **GB200 Support**: Added support for GB200 GPU with ARM architecture
* **Extended Sequence Processing**:
  * PyTorch backend supports up to 4096 length sequences
  * TensorRT backend supports up to 2048 length sequences
* Enhanced compatibility with ARM-based systems

### Notes and Limitations

* Ensure you use this NIM with GPUs with at least 48 GB of VRAM.
* **CUDA Driver Requirements**:
  * Minimum version 580 with CUDA 13.0
* **TensorRT Engine Speedup**: To achieve optimal prediction speedup using TRT engines, this version uses 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.

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