BioNeMo RFD4-Proteina NIM Overview

Description:

RFD4-Proteina NIM is a containerized deployment of RFD4-Proteina, a unified\ngenerative model for protein design produced through a collaboration between\nNVIDIA's Generative AI team and the David Baker Lab at the Institute for Protein\nDesign. The container provides accelerated inference, a stable API, NVIDIA\nhardware-matrix compatibility and NVIDIA enterprise support.\n

The service exposes seven design tasks — unconditional generation, motif\nscaffolding, enzyme design, small-molecule binder design, antibody design,\npartial diffusion and inverse folding — and reward-guided beam search over the\ndiffusion trajectory, so a caller can optimize designs against locally\ncomputable metrics rather than sampling a single unranked output.\n

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

Third-Party Community Consideration

This model is not owned or developed by NVIDIA. This model has been developed and\nbuilt to a third-party's requirements for this application and use case. The\ncontainer includes the third-party RFD4-Proteina model\n(RFD4-Proteina model card).

License / Terms of Use

GOVERNING TERMS: Your use of the NIM container is governed by the NVIDIA Software License Agreement and Product-Specific Terms for AI Products. Your use of the model is governed by the NVIDIA Community Model License. ADDITIONAL INFORMATION: BSD 3-Clause License.

This container is licensed under the NVIDIA AI Product Agreement. By pulling and using this container, you accept the terms and conditions of this license.

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

NOTICE AND DISCLAIMER: This software automatically retrieves, accesses or\ninteracts with external materials. Those retrieved materials are not\ndistributed with this software and are governed solely by separate terms,\nconditions and licenses. You are solely responsible for finding, reviewing and\ncomplying with all applicable terms, conditions, and licenses, and for\nverifying the security, integrity and suitability of any retrieved materials\nfor your specific use case. This software is provided \"AS IS\", without warranty\nof any kind. The author makes no representations or warranties regarding any\nretrieved materials, and assumes no liability for any losses, damages,\nliabilities or legal consequences from your use or inability to use this\nsoftware or any retrieved materials. Use this software and the retrieved\nmaterials at your own risk.

Deployment Geography

Global

Use Case

The RFD4-Proteina NIM is used by academic and pharmaceutical industry research\nlabs for de novo protein, enzyme, binder and antibody design, supporting\nearly-stage biologic discovery: scaffolding a functional site, designing a binder\nagainst a known target, or resampling a sequence for an existing backbone.

Release Date

build.nvidia.com 10/20/2026 (NVIDIA GTC Berlin) via build.nvidia.com/ipd/rfd4-proteina
NGC 10/20/2026 (NVIDIA GTC Berlin) via catalog.ngc.nvidia.com/orgs/nim/teams/ipd/containers/rfd4-proteina

Program Classes:

The NIM contains the RFD4-Proteina model, including RFD4-Proteina inference code\nand model weights (checkpoints) for both the latent-diffusion trunk and the\npaired autoencoder. The checkpoints are pulled automatically at NIM startup.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

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

Model Name & Link	Use Case	How to Pull the Model
RFD4-Proteina	Generate de novo protein backbones and sequences for binder, enzyme, antibody and scaffold design	Automatic

Deployment Details:

The RFD4-Proteina NIM is deployed by pulling and running the container in an\nenvironment with appropriate credentials. For instructions to pull and run,\nhardware requirements, and the NVIDIA GPU support matrix, see RFD4-Proteina NIM Docs.

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

This NIM ships with no refolding oracle built in. A deployment can mount an\noperator-provided reward.py to call out to an independently hosted\nstructure-prediction model for in-silico pass/fail scoring and reward-guided\nsearch; refer to the NIM Docs for the mount contract.

Supported Hardware

Validated on 19 NVIDIA GPU SKUs spanning the Ampere, Hopper, Ada Lovelace,\nBlackwell and Grace Blackwell architectures, from NVIDIA L40S and A100 through\nH100, H200, B200, B300, GB200, GB300 and NVIDIA DGX Spark (GB10). See the\nRFD4-Proteina NIM Docs support matrix\nfor the full, per-SKU list. The container image is built for amd64 (x86_64)\nonly; there is no arm64 (aarch64) image today.

References:

@article{Watson2023,\n  author  = {Watson, Joseph L. and Juergens, David and Bennett, Nathaniel R. and Trippe, Brian L. and Yim, Jason and Eisenach, Helen E. and Ahern, Woody and Borst, Andrew J. and Ragotte, Robert J. and Milles, Lukas F. and Wicky, Basile I. M. and Hanikel, Nikita and Pellock, Samuel J. and Courbet, Alexis and Sheffler, William and Wang, Jue and Venkatesh, Preetham and Sappington, Isaac and Torres, Susana Vázquez and Lauko, Anna and De Bortoli, Valentin and Mathieu, Emile and Ovchinnikov, Sergey and Barzilay, Regina and Jaakkola, Tommi S. and DiMaio, Frank and Baek, Minkyung and Baker, David},\n  journal = {Nature},\n  title   = {De novo design of protein structure and function with RFdiffusion},\n  year    = {2023},\n  volume  = {620},\n  pages   = {1089--1100},\n  doi     = {10.1038/s41586-023-06415-8}\n}\n

\n
\n

Container Version(s):

v1.0.0

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: RFD4-Proteina NIM Docs.

Enterprise Support

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

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\n\nThe service exposes seven design tasks — unconditional generation, motif\nscaffolding, enzyme design, small-molecule binder design, antibody design,\npartial diffusion and inverse folding — and reward-guided beam search over the\ndiffusion trajectory, so a caller can optimize designs against locally\ncomputable metrics rather than sampling a single unranked output.\n
\n\nThe container components are ready for commercial or non-commercial use.\n\n### Third-Party Community Consideration\n\nThis model is not owned or developed by NVIDIA. This model has been developed and\nbuilt to a third-party's requirements for this application and use case. The\ncontainer includes the third-party RFD4-Proteina model\n([RFD4-Proteina model card](https://build.nvidia.com/ipd/rfd4-proteina/modelcard)).\n\n#### License / Terms of Use\n\nGOVERNING TERMS: Your use of the NIM 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/). Your use of the model is governed by the [NVIDIA Community Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/). ADDITIONAL INFORMATION: [BSD 3-Clause License](https://opensource.org/licenses/BSD-3-Clause).\n\nThis container is licensed under the [NVIDIA AI Product Agreement](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/). By pulling and using this container, you accept the terms and conditions of this license.\n\n**You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.**\n\nNOTICE AND DISCLAIMER: This software automatically retrieves, accesses or\ninteracts with external materials. Those retrieved materials are not\ndistributed with this software and are governed solely by separate terms,\nconditions and licenses. You are solely responsible for finding, reviewing and\ncomplying with all applicable terms, conditions, and licenses, and for\nverifying the security, integrity and suitability of any retrieved materials\nfor your specific use case. This software is provided \"AS IS\", without warranty\nof any kind. The author makes no representations or warranties regarding any\nretrieved materials, and assumes no liability for any losses, damages,\nliabilities or legal consequences from your use or inability to use this\nsoftware or any retrieved materials. Use this software and the retrieved\nmaterials at your own risk.\n\n### Deployment Geography\nGlobal\n\n### Use Case\nThe RFD4-Proteina NIM is used by academic and pharmaceutical industry research\nlabs for de novo protein, enzyme, binder and antibody design, supporting\nearly-stage biologic discovery: scaffolding a functional site, designing a binder\nagainst a known target, or resampling a sequence for an existing backbone.\n\n### Release Date\n- build.nvidia.com 10/20/2026 (NVIDIA GTC Berlin) via [build.nvidia.com/ipd/rfd4-proteina](https://build.nvidia.com/ipd/rfd4-proteina)\n- NGC 10/20/2026 (NVIDIA GTC Berlin) via [catalog.ngc.nvidia.com/orgs/nim/teams/ipd/containers/rfd4-proteina](https://catalog.ngc.nvidia.com/orgs/nim/teams/ipd/containers/rfd4-proteina)\n\n## Program Classes:\nThe NIM contains the RFD4-Proteina model, including RFD4-Proteina inference code\nand model weights (checkpoints) for both the latent-diffusion trunk and the\npaired autoencoder. The checkpoints are pulled automatically at NIM startup.\n\n| Model Name & Link | Use Case | How to Pull the Model |\n| :--- | :--- | :--- |\n| [RFD4-Proteina](https://build.nvidia.com/ipd/rfd4-proteina/modelcard) | Generate de novo protein backbones and sequences for binder, enzyme, antibody and scaffold design | Automatic |\n\n\n## Deployment Details:\n\nThe RFD4-Proteina NIM is deployed by pulling and running the container in an\nenvironment with appropriate credentials. For instructions to pull and run,\nhardware requirements, and the NVIDIA GPU support matrix, see [RFD4-Proteina NIM Docs](https://docs.nvidia.com/nim/bionemo/rfd4-proteina/latest/index.html).\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated\nsystems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks\n(e.g., CUDA libraries), the model achieves faster inference times compared to\nCPU-only solutions.\n\nThis NIM ships with no refolding oracle built in. A deployment can mount an\noperator-provided `reward.py` to call out to an independently hosted\nstructure-prediction model for in-silico pass/fail scoring and reward-guided\nsearch; refer to the NIM Docs for the mount contract.\n\n### Supported Hardware\n\nValidated on 19 NVIDIA GPU SKUs spanning the Ampere, Hopper, Ada Lovelace,\nBlackwell and Grace Blackwell architectures, from NVIDIA L40S and A100 through\nH100, H200, B200, B300, GB200, GB300 and NVIDIA DGX Spark (GB10). See the\n[RFD4-Proteina NIM Docs support matrix](https://docs.nvidia.com/nim/bionemo/rfd4-proteina/latest/support-matrix.html)\nfor the full, per-SKU list. The container image is built for **amd64 (x86_64)\nonly**; there is no arm64 (aarch64) image today.\n\n### References:\n\n```\n@article{Watson2023,\n author = {Watson, Joseph L. and Juergens, David and Bennett, Nathaniel R. and Trippe, Brian L. and Yim, Jason and Eisenach, Helen E. and Ahern, Woody and Borst, Andrew J. and Ragotte, Robert J. and Milles, Lukas F. and Wicky, Basile I. M. and Hanikel, Nikita and Pellock, Samuel J. and Courbet, Alexis and Sheffler, William and Wang, Jue and Venkatesh, Preetham and Sappington, Isaac and Torres, Susana Vázquez and Lauko, Anna and De Bortoli, Valentin and Mathieu, Emile and Ovchinnikov, Sergey and Barzilay, Regina and Jaakkola, Tommi S. and DiMaio, Frank and Baek, Minkyung and Baker, David},\n journal = {Nature},\n title = {De novo design of protein structure and function with RFdiffusion},\n year = {2023},\n volume = {620},\n pages = {1089--1100},\n doi = {10.1038/s41586-023-06415-8}\n}\n```\n\n
\n\n## Container Version(s):\n\nv1.0.0
\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: [RFD4-Proteina NIM Docs](https://docs.nvidia.com/nim/bionemo/rfd4-proteina/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/).",["D",1792508723236],[279,275,212,277],"/api/image/nim/ipd/containers/rfd4-proteina",18607953352,{"_325":326,"_327":328,"_329":330,"_331":26,"_332":333},"hasSignedTag",[316,317],[307,308],[],[299],[],{"_93":300,"_301":300,"_302":303,"_304":305},"signed images","value","filter",{"_306":305,"_301":300},"category","system","field",{"_93":312,"_301":313,"_302":314,"_304":311},{"_93":309,"_301":309,"_302":310,"_304":311},"NSPECT-T6XW-VX8R",{"_306":311,"_301":309},"label","Clara","pltfm_clara",{"_306":315,"_301":313},"platform",{"_93":323,"_301":323,"_302":324,"_304":321},{"_93":318,"_301":319,"_302":320,"_304":321},"NVIDIA NIM","nimmcro_nvidia_nim",{"_306":322,"_301":319},"feature","nvidia_nim","NVIDIA AI Enterprise Supported",{"_306":311,"_301":323},"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 BSD 3-Clause License.

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GOVERNING HOSTING TERMS: Use of this trial service is governed by the NVIDIA API Trial Terms of Service. Use of the model is governed by the BSD 3-Clause License.

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It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","omniverse.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. 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. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","nvidiaCart","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","nv_co","__uxq412__ses.88c1","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","learn.learn.nvidia.com","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","This cookie is associated with Akamai. 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