> This page is for OpenFold2, version 2.3.0.
> For other versions, use one of these documentation indexes:
> - 2.6.0 (Latest) (default): https://docs.nvidia.com/nim/bionemo/openfold2/2.6.0/llms.txt
> - 2.5.0: https://docs.nvidia.com/nim/bionemo/openfold2/2.5.0/llms.txt
> - 2.4.0: https://docs.nvidia.com/nim/bionemo/openfold2/2.4.0/llms.txt
> - 2.3.0: https://docs.nvidia.com/nim/bionemo/openfold2/2.3.0/llms.txt
> - 2.2.0: https://docs.nvidia.com/nim/bionemo/openfold2/2.2.0/llms.txt
> - 2.1.0: https://docs.nvidia.com/nim/bionemo/openfold2/2.1.0/llms.txt
> - 2.0.0: https://docs.nvidia.com/nim/bionemo/openfold2/2.0.0/llms.txt
> - 1.2.0: https://docs.nvidia.com/nim/bionemo/openfold2/1.2.0/llms.txt
> - 1.1.0: https://docs.nvidia.com/nim/bionemo/openfold2/1.1.0/llms.txt
> - 1.0.0: https://docs.nvidia.com/nim/bionemo/openfold2/1.0.0/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

## February 2, 2026

## Release 2.3.0

### Summary

This release extends GPU support with the addition of the GB10 architecture and updates cuEquivariance for enhanced performance.

### Key Features

* **GB10 DGX Spark support**: Added support for GB10 DGX Spark SKUs with sequence lengths up to 1536 residues
* **cuEquivariance integration**: Updated to cuEquivariance [0.8.1](https://github.com/NVIDIA/cuEquivariance/releases/tag/v0.8.1) for GB10 support

## January 7, 2026

## Release 2.2.0

### Summary

This release extends GPU support with the addition of the GB200 architecture.

### Key Features

* **GB200 Support**: Added TensorRT-BioNeMo support for GB200 SKUs with sequence lengths up to 2048 residues

## November 26, 2025

## Release 2.1.0

### Summary

This release focuses on security improvements, performance enhancements for newer GPU architectures, and extended sequence length support.

### Key Features

* **Security**: Addressed all CVE (Common Vulnerabilities and Exposures) issues
* **cuEquivariance integration**: Updated to cuEquivariance [0.7.0](https://github.com/NVIDIA/cuEquivariance/releases/tag/v0.7.0) for improved equivariant operations
* **Enhanced B200 support**: Enabled trimul kernel optimization for B200 SKUs in PyTorch backend
* **Extended sequence length**: Support for longer sequences up to 2048 residues on A100, H100, and B200 GPUs with TensorRT-BioNeMo
* **Telemetry control**: NIM Telemetry helps NVIDIA deliver a faster, more reliable experience with greater compatibility across a wide range of environments, while maintaining strict privacy protections and giving users full control.

  **Benefits:**

  * **Enhances performance and reliability**: Provides anonymous system and NIM-level insights that help NVIDIA identify bottlenecks, tune performance across hardware configurations, and improve runtime stability.
  * **Improves compatibility across deployments**: Helps detect and resolve version, driver, and environment compatibility issues early, reducing friction across diverse infrastructure setups.
  * **Accelerates troubleshooting and bug resolution**: Allows NVIDIA to diagnose errors and regressions faster, leading to quicker support response times and higher overall availability.
  * **Informs smarter optimizations and future releases**: Real-world, aggregated telemetry data helps guide the optimization of NIM runtimes, model packaging, and deployment workflows, ensuring updates target the scenarios that matter most to users.
  * **Protects user privacy and data security**: Collects only minimal, anonymous metadata, such as hardware type and NIM version. No user data, input sequences, or prediction results are collected.
  * **Fully optional and configurable**: Telemetry collection is disabled by default. You can toggle telemetry at any time using environment variables.

  **Configuration:**

  * Set `NIM_TELEMETRY_MODE=0` to disable telemetry (default)
  * Set `NIM_TELEMETRY_MODE=1` to enable telemetry

  For more information about data privacy, what is collected, and how to configure telemetry, refer to:

  * [NVIDIA's Privacy Policy](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/)
  * [NIM Telemetry Settings](https://developer.nvidia.com/docs/nim-tools/latest/templates/nimlib/nim-telemetry.html)

## October 10, 2025

## Release 2.0.0

### Summary

This release removes HHR-based template processing in favor of explicit mmCIF template support and adds TensorRT integration for new GPU architectures.

### Key Changes

* **Enhanced GPU support**: TensorRT integration for L40S, B200, and RTX 6000 Ada Generation

* **Removed**: Removed the following functionalities:
  * The downloading and decompressing of a bundled database of structure files at NIM startup
  * The web API accepting input fields containing HHR content
  * The input data processing code that uses the HHR content to select template structures from the de-compressed database of structure files

* **Simplified**: Template workflow now uses only mmCIF format using `explicit_templates`, where users can submit one or multiple mmCIF strings as part of their structure prediction requests.

* **Reduced footprint**: Smaller container size and faster startup without bundled databases

* **Migration guide**: Refer to the [Migration Guide](/nim/bionemo/openfold2/2.3.0/migrating-from-hhr-to-explicit-templates) for upgrading from previous versions

## September 2, 2025

## Release 1.2.0

### Summary

This release introduces support for user-supplied mmCIF input features.

### Key Features

* Users can now submit one or multiple mmCIF strings as part of their structure prediction requests.
* The structure prediction network can utilize these user-supplied mmCIF inputs for enhanced flexibility and custom workflows.
* This feature enables new use cases where users provide their own structural templates or data in mmCIF format.

## May 28, 2025

## Release 1.1.0

### Summary

This release introduces significant performance improvements through TensorRT (TRT) optimization.

### Key Features

* Enhanced inference performance using TensorRT optimization
* Improved model execution speed
* Optimized memory usage during inference

## March 17, 2025

## Release 1.0.0

### Summary

This is the first release of NVIDIA NIM for OpenFold2.