BioNeMo Boltz2 NIM Overview

Description:

Boltz-2 NIM is a next-generation structural biology foundation model that shows strong performance for both structure and affinity prediction. Boltz-2 is the first deep learning model to approach the accuracy of free energy perturbation (FEP) methods in predicting binding affinities of small molecules and proteins—achieving strong correlations on benchmarks while being nearly 1000× more computationally efficient.\n

The container components are ready for commercial or non-commercial use.\n

Third-Party Community Consideration

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case.

License / Terms of Use

GOVERNING DOWNLOAD TERMS: Use of this container is governed by the NVIDIA Software License Agreement and Product-Specific Terms for AI Products. Use of the model is governed by the NVIDIA Open Model Agreement.\nADDITIONAL INFORMATION: Apache License, Version 2.0 and MIT License.

You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.

Deployment Geography

Global

Use Case

Boltz-2 NIM enables researchers and commercial entities in the Drug Discovery, Life Sciences, and Digital Biology fields to predict the three-dimensional structure of biomolecular complexes and predict small-molecule binding affinities. Trained on millions of curated experimental datapoints with a novel training strategy tailored for noisy biochemical assay data, Boltz-2 demonstrates robust performance across hit-discovery, hit-to-lead, and lead optimization.

Release Date

build.nvidia.com: 9/29/2026 via build.nvidia.com/mit/boltz2
\nNGC: 9/29/2026 via catalog.ngc.nvidia.com

Program Classes:

The NIM contains the Boltz2 model, including Boltz2 inference code, TRT-supporting code, model weights (checkpoint), and TRT engines. The model (checkpoint) and TRT engines are pulled automatically at NIM startup.

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tr:nth-child(odd)]:bg-surface-sunken\">

Model Name & Link	Use Case	How to Pull the Model
Boltz2. Model Card	Predict the structure proteins, DNA, RNA, and ligands, including as complexes	Automated

Deployment Details:

The Boltz2 NIM is deployed by pulling and running the container in an environment with appropriate credentials. For instructions to pull and run, hardware requirements, and NVIDIA GPU support matrix, see Boltz2 NIM Docs.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster inference times compared to CPU-only solutions

References:

@article{wohlwend2024boltz,\n    title = {Boltz-1: Democratizing Biomolecular Interaction Modeling},\n    author = {Wohlwend, Jeremy and Corso, Gabriele and Passaro, Saro and Getz, Noah and Reveiz, Mateo and Leidal, Ken and Swiderski, Wojtek and Atkinson, Liam and Portnoi, Tally and Chinn, Itamar and Silterra, Jacob and Jaakkola, Tommi and Barzilay, Regina},\n    journal = {bioRxiv},\n    year = {2024},\n    doi = {10.1101/2024.11.19.624167},\n    language = \"en\"\n}\n

\n
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Container Version(s):

Boltz2 v1.10.0

Security Common Vulnerabilities and Exposures (CVEs)

Please review the Security Scanning tab on NGC to view the latest security scan results.\nFor certain open-source vulnerabilities listed in the scan results, NVIDIA provides a response in the form of a Vulnerability Exploitability eXchange (VEX) document. The VEX information can be reviewed and downloaded from the Security Scanning tab.

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal developer team to ensure these software components meet requirements for the relevant industry and use case and address unforeseen product misuse.

Users are responsible for ensuring the physical properties of model-generated molecules are appropriately evaluated and comply with applicable safety regulations and ethical standards.

Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here..

Get Help

Getting started with the NIM

Deploying and integrating the NIM is straightforward thanks to our industry standard APIs. Visit the NIM Container page for release documentation, deployment guides and more Boltz2 NIM Docs.

Enterprise Support

Get access to knowledge base articles and support cases or submit a ticket.

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\n\nThe container components are ready for commercial or non-commercial use.\n
\n\n### Third-Party Community Consideration\n\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case.\n\n#### License / Terms of Use\n\nGOVERNING DOWNLOAD TERMS: Use of this container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and [Product-Specific Terms for AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/). Use of the model is governed by the [NVIDIA Open Model Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/).\n**ADDITIONAL INFORMATION:** [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) and [MIT License](https://github.com/jwohlwend/boltz/blob/main/LICENSE).\n\n**You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.**\n\n### Deployment Geography\nGlobal\n\n### Use Case\nBoltz-2 NIM enables researchers and commercial entities in the Drug Discovery, Life Sciences, and Digital Biology fields to predict the three-dimensional structure of biomolecular complexes and predict small-molecule binding affinities. Trained on millions of curated experimental datapoints with a novel training strategy tailored for noisy biochemical assay data, Boltz-2 demonstrates robust performance across hit-discovery, hit-to-lead, and lead optimization.\n\n### Release Date\nbuild.nvidia.com: 9/29/2026 via [build.nvidia.com/mit/boltz2](https://build.nvidia.com/mit/boltz2)
\nNGC: 9/29/2026 via [catalog.ngc.nvidia.com](https://catalog.ngc.nvidia.com/orgs/nim/teams/mit/containers/boltz2)
\n\n## Program Classes:\nThe NIM contains the Boltz2 model, including Boltz2 inference code, TRT-supporting code, model weights (checkpoint), and TRT engines. The model (checkpoint) and TRT engines are pulled automatically at NIM startup.\n\n| Model Name & Link | Use Case | How to Pull the Model |\n| :--- | :--- | :--- |\n| Boltz2. [Model Card](https://build.nvidia.com/mit/boltz2/modelcard) | Predict the structure proteins, DNA, RNA, and ligands, including as complexes | Automated |\n\n\n## Deployment Details: \n\nThe Boltz2 NIM is deployed by pulling and running the container in an environment with appropriate credentials. For instructions to pull and run, hardware requirements, and NVIDIA GPU support matrix, see [Boltz2 NIM Docs](https://docs.nvidia.com/nim/bionemo/boltz2/latest/overview.html).\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster inference times compared to CPU-only solutions\n\n### References:\n\n```\n@article{wohlwend2024boltz,\n title = {Boltz-1: Democratizing Biomolecular Interaction Modeling},\n author = {Wohlwend, Jeremy and Corso, Gabriele and Passaro, Saro and Getz, Noah and Reveiz, Mateo and Leidal, Ken and Swiderski, Wojtek and Atkinson, Liam and Portnoi, Tally and Chinn, Itamar and Silterra, Jacob and Jaakkola, Tommi and Barzilay, Regina},\n journal = {bioRxiv},\n year = {2024},\n doi = {10.1101/2024.11.19.624167},\n language = \"en\"\n}\n```\n\n
\n\n\n## Container Version(s): \n\nBoltz2 v1.10.0
\n\n## Security Common Vulnerabilities and Exposures (CVEs)\nPlease review the Security Scanning tab on NGC to view the latest security scan results.\nFor certain open-source vulnerabilities listed in the scan results, NVIDIA provides a response in the form of a Vulnerability Exploitability eXchange (VEX) document. The VEX information can be reviewed and downloaded from the Security Scanning tab.\n\n## Ethical Considerations:\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal developer team to ensure these software components meet requirements for the relevant industry and use case and address unforeseen product misuse. \n\nUsers are responsible for ensuring the physical properties of model-generated molecules are appropriately evaluated and comply with applicable safety regulations and ethical standards.
\n\nPlease report quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://app.intigriti.com/programs/nvidia/nvidiavdp/detail)..\n\n# Get Help\n\n## Getting started with the NIM\nDeploying and integrating the NIM is straightforward thanks to our industry standard APIs. Visit the NIM Container page for release documentation, deployment guides and more [Boltz2 NIM Docs](https://docs.nvidia.com/nim/bionemo/boltz2/latest/index.html).\n\n## Enterprise Support \nGet access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).","Boltz-2",["D",1791095277618],[331,325,323,321,319,317,315,313,311,309,307,305,303,301,299,297,295,293,291,289,329,287,208,327],"/api/image/nim/mit/containers/boltz2",12467531187,{"_368":369,"_370":371,"_372":373,"_374":26,"_375":376},"hasSignedTag",[359,360],[356],[],[352],[],{"_91":353,"_142":353,"_143":354,"_145":355},"signed images",{"_147":355,"_142":353},"system",{"_91":357,"_142":357,"_143":358,"_145":146},"NSPECT-D4IX-8I2O",{"_147":146,"_142":357},{"_91":366,"_142":366,"_143":367,"_145":364},{"_91":361,"_142":362,"_143":363,"_145":364},"NVIDIA NIM","nimmcro_nvidia_nim",{"_147":365,"_142":362},"feature","nvidia_nim","NVIDIA AI Enterprise Supported",{"_147":146,"_142":366},"governingTerms","

GOVERNING DOWNLOAD TERMS: Use of this container is governed by the NVIDIA Software License Agreement and Product-Specific Terms for NVIDIA AI Products. Use of the model is governed by the NVIDIA Open Model Agreement. ADDITIONAL INFORMATION: Apache License, Version 2.0 and MIT License.

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users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. 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This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. 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