Release Notes#

Release 1.4.0#

Summary#

This release addresses security vulnerabilities, integrates the latest cuEquivariance library, and introduces new features including telemetry control and confidence score persistence capabilities.

Key Features#

  • Security: Addressed all CVE (Common Vulnerabilities and Exposures) issues

  • cuEquivariance integration: Updated to cuEquivariance 0.7.0 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

  • Confidence score persistence: Implemented Docker volume mounting flag to expose output directories with per-sample confidence scores, enabling result persistence in NIM cache folder. Configure using:

    • Set NIM_EXPOSE_CONFIDENCE_SCORES=true to enable confidence score output

    • Set NIM_EXPOSE_CONFIDENCE_SCORES=false to disable (default)

  • 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:

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 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 1.2.0#

Summary#

This release introduces support for longer sequence processing and additional GPU compatibility, along with performance optimizations.

Key Features#

  • Support longer sequence (2048 length) processing for TRT engines on A100, H100, and B200 with memory larger than 48GB

  • Support PyTorch and TRT engines with NVIDIA RTX6000 (RTX6000 and RTX6000-Ada GPU Workstation Edition GPU)

  • Improve model execution speed

  • Optimize memory usage during inference

Notes and Limitations#

  • Ensure you use this NIM with GPUs with at least 48 GB of VRAM.

  • TensorRT Engine Speedup: To achieve optimal prediction speedup using TRT engines, this version utilizes TensorRT 10.11.0. Refer to the TensorRT 10.11.0 Support Matrix to ensure your CUDA Driver and CUDA Toolkit versions are compatible.

    CUDA Requirements for TensorRT 10.11.0:

    • CUDA Driver: 12.9, 12.8 update 1, 12.6 update 3, 12.5 update 1, 12.4 update 1, 12.3 update 2, 12.2 update 2, 12.1 update 1, 12.0 update 1, 11.8, 11.7 update 1, 11.6 update 2, 11.5 update 2, 11.4 update 4, 11.3 update 1, 11.2 update 2, 11.1 update 1, 11.0 update 3

    • CUDA Toolkit: Must be compatible with the chosen CUDA Driver version

    • Platform Support: Linux x86-64, Windows x64, Linux SBSA, and NVIDIA JetPack

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#

Boltz-2 enables accurate biomolecular structure prediction from input sequences including proteins, DNA, RNA, and ligands. It also supports constrained guidance for complex structural prediction using bond and pocket specifications.

This release adds binding affinity prediction for ligands and complexes. The underlying Boltz-2 model has been updated to version 2.2.0. More of the Boltz-2 scores are included in the output. There are minor bug fixes to improve the overall user experience.

Notes and Limitations#

Ensure you use this NIM with GPUs with at least 48 GB of VRAM.

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