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Release Notes for NVIDIA NIM for OpenFold3#

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 for GB10 support

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 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.

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:

Supported Features#

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

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)