> This page is for Boltz-2, version 1.10.0 (Latest) (default).
> For other versions, use one of these documentation indexes:
> - 1.10.0 (Latest) (default): https://docs.nvidia.com/nim/bionemo/boltz2/1.10.0/llms.txt
> - 1.9.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.9.0/llms.txt
> - 1.8.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.8.0/llms.txt
> - 1.7.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.7.0/llms.txt
> - 1.6.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.6.0/llms.txt
> - 1.5.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.5.0/llms.txt
> - 1.4.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.4.0/llms.txt
> - 1.3.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.3.0/llms.txt
> - 1.2.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.2.0/llms.txt
> - 1.1.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.1.0/llms.txt
> - 1.0.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.0.0/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 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](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

* **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:

  * [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)