Release Notes

Summary

This release addresses security vulnerabilities, integrates BioNeMo Inference Runtime 0.2.0, and updates nimlib. Predictions remain compatible with 2.6.0, with a modest improvement in inference performance.

Key Features

• Security: Addresses all CVE (Common Vulnerabilities and Exposures) issues.
• BioNeMo Inference Runtime 0.2.0: Updates the optimized prediction path to BioNeMo Inference Runtime 0.2.0, with a modest improvement in inference performance.
• nimlib: Updates nimlib.

Summary

This release moves the whole prediction path onto the BioNeMo Inference Runtime. Parsing, MSA and template featurization, and structure writing were previously handled by OpenFold code with the runtime used only as an optimized model. They are now handled by the runtime end to end. Predictions are unchanged within measurement error, and the container is substantially smaller.

Summary

This release extends GPU support with NVIDIA B300 and NVIDIA GB300, and removes the per-GPU engine-build step that was required by earlier releases. OpenFold2 now starts and serves requests with a single, simplified runtime configuration.

Key Features

Summary

This release extends GPU support with the addition of NVIDIA GH200 144GB superchips and the NVIDIA RTX PRO 6000 Blackwell Workstation Edition, adds Slurm support for HPC deployments, and adds support for NVIDIA H200.

Key Features

• NVIDIA H200: Added support for NVIDIA H200 with 141GB memory
• NVIDIA GH200 144GB: Added support for NVIDIA GH200 144GB 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 (refer to Running on Slurm With Enroot in Prerequisites)

Summary

This release extends GPU support with the addition of the NVIDIA GB10 (DGX Spark) architecture and updates cuEquivariance for enhanced performance.

Key Features

• NVIDIA GB10 (DGX Spark) support: Added support for NVIDIA GB10 (DGX Spark) SKUs with sequence lengths up to 1536 residues
• cuEquivariance integration: Updated to cuEquivariance 0.8.1 for NVIDIA GB10 (DGX Spark) support

Summary

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

Key Features

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

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 for improved equivariant operations

• Enhanced NVIDIA B200 support: Enabled trimul kernel optimization for NVIDIA B200 SKUs in PyTorch backend

• Extended sequence length: Support for longer sequences up to 2048 residues on NVIDIA A100-SXM4-80GB, NVIDIA H100 80GB HBM3, and NVIDIA 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
  • NIM Telemetry Settings

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 NVIDIA L40S, NVIDIA B200, and NVIDIA 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 for upgrading from previous versions

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.

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

Summary

This is the first release of NVIDIA NIM for OpenFold2.