> This page is for OpenFold3, version 1.4.0.
> For other versions, use one of these documentation indexes:
> - 1.6.0 (Latest) (default): https://docs.nvidia.com/nim/bionemo/openfold3/1.6.0/llms.txt
> - 1.5.0: https://docs.nvidia.com/nim/bionemo/openfold3/1.5.0/llms.txt
> - 1.4.0: https://docs.nvidia.com/nim/bionemo/openfold3/1.4.0/llms.txt
> - 1.3.0: https://docs.nvidia.com/nim/bionemo/openfold3/1.3.0/llms.txt
> - 1.2.0: https://docs.nvidia.com/nim/bionemo/openfold3/1.2.0/llms.txt
> - 1.1.0: https://docs.nvidia.com/nim/bionemo/openfold3/1.1.0/llms.txt
> - 1.0.0: https://docs.nvidia.com/nim/bionemo/openfold3/1.0.0/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

## March 16, 2026

## Release 1.4.0

### Summary

This release extends GPU support with the addition of NVIDIA GH200 superchips and the RTX PRO 6000 Blackwell Workstation Edition, adds Slurm support for HPC deployments, and introduces a new checkpoint from OpenFold lab that improves prediction accuracy.

### Key Features

* **NVIDIA GH200 144GB**: Added support for GH200 with 144GB memory
* **NVIDIA RTX PRO 6000 Blackwell Workstation Edition**: Added support for the professional workstation GPU with 96GB GDDR7 memory
* **Slurm support**: Added support for running on Slurm-based HPC clusters
* **New checkpoint from OpenFold lab**: Added support for a new checkpoint that improves prediction accuracy

## February 2, 2026

## Release 1.3.0

### 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 8, 2026

## Release 1.2.0

### Summary

This release adds support for GB200 GPU with ARM architecture and extends hardware compatibility for enterprise deployments.

### Key Features

* **GB200 Support**: Added support for GB200 GPU
* 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.

## November 26, 2025

## Release 1.1.0

### Summary

This release adds support for protein structural template inputs for complex structure prediction, and additional GPU hardware.

### New Features

#### Structural Template Support

The NIM now accepts protein structural template inputs (single-chain) to guide structure prediction:

* **Template input**: For each input, protein molecules can include structural templates in CIF format
* **Template guidance**: Templates help constrain and improve structure predictions when experimental or predicted structures are available
* **Multiple templates**: Each protein can have multiple structural templates

#### Additional GPU Support

Support for additional NVIDIA GPU configurations:

* **NVIDIA B200**: High-performance GPU for large-scale structure prediction
* **NVIDIA L40S**: Cost-effective GPU option for production deployments

#### Telemetry Control

**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)

### Supported Features

All features from release 1.0.0 remain supported, with the addition of structural templates for protein molecules.

## October 27, 2025

## Release 1.0.0

### Summary

This is the first release of NVIDIA NIM for OpenFold3.

### Supported Features

#### Entity Types

The NIM request API accepts the following molecular entity types:

* **Protein**: Amino acid sequences
* **DNA**: DNA sequences
* **RNA**: RNA sequences
* **Ligand**: Small molecules specified via CCD codes or SMILES strings

#### Multiple Entities and Complexes

The API accepts inputs with multiple entities across multiple types, enabling
prediction of complex biomolecular structures. For example:

* Monomeric proteins: 1 protein sequence
* Protein-ligand complexes: 1 or more protein sequences, 1 or more ligands
* Homomeric protein complexes: 2 or more identical protein sequences
* Heteromeric protein complexes: 2 or more nonidentical protein sequences
* Protein-DNA complexes: 1 or more protein sequences, 1 or more DNA sequences
* Protein-RNA complexes: 1 or more protein sequences, 1 or more RNA sequences

#### Multiple Sequence Alignment (MSA) Support

The NIM accepts MSA inputs with flexible configuration:

* **MSA Requirements**: MSA inputs are required for protein and RNA entity types
* **Format support**: Multiple alignment file formats are supported:
  * `a3m` : A3M format
  * `csv` : CSV format
* **MSA types**:
  * Unpaired MSAs for protein sequences and RNA sequences.
  * Paired MSAs for inputs with multiple non-identical protein sequences.

Note: The OpenFold3 NIM accepts paired MSA inputs, but does **not** perform 'online' pairing of MSAs.

#### Inference Parameters

**Forward Pass Parameters:**

* `diffusion_samples`: Number of independent structures to generate (1-5, default: 1)

#### Structure Templates

* **Note**: This release does not support structure template inputs. Predictions are generated without template guidance.

#### Output Formats

* **CIF**: Crystallographic Information File format (supported)
* **PDB**: Protein Data Bank format (supported)
* **mmCIF**: Macromolecular CIF format (not yet supported)