BioNeMo RFD4-Proteina NIM Overview

Description:

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

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

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 built to a third-party's requirements for this application and use case. The container includes the third-party RFD4-Proteina model (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 interacts with external materials. Those retrieved materials are not distributed with this software and are governed solely by separate terms, conditions and licenses. You are solely responsible for finding, reviewing and complying with all applicable terms, conditions, and licenses, and for verifying the security, integrity and suitability of any retrieved materials for your specific use case. This software is provided "AS IS", without warranty of any kind. The author makes no representations or warranties regarding any retrieved materials, and assumes no liability for any losses, damages, liabilities or legal consequences from your use or inability to use this software or any retrieved materials. Use this software and the retrieved materials at your own risk.

Deployment Geography

Global

Use Case

The RFD4-Proteina NIM is used by academic and pharmaceutical industry research labs for de novo protein, enzyme, binder and antibody design, supporting early-stage biologic discovery: scaffolding a functional site, designing a binder against 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 and model weights (checkpoints) for both the latent-diffusion trunk and the paired autoencoder. The checkpoints are pulled automatically at NIM startup.

Deployment Details:

The RFD4-Proteina NIM is deployed by pulling and running the container in an environment with appropriate credentials. For instructions to pull and run, hardware 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 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.

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

Supported Hardware

Validated on 19 NVIDIA GPU SKUs spanning the Ampere, Hopper, Ada Lovelace, Blackwell and Grace Blackwell architectures, from NVIDIA L40S and A100 through H100, H200, B200, B300, GB200, GB300 and NVIDIA DGX Spark (GB10). See the RFD4-Proteina NIM Docs support matrix for the full, per-SKU list. The container image is built for amd64 (x86_64) only; there is no arm64 (aarch64) image today.

References:

@article{Watson2023,
  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},
  journal = {Nature},
  title   = {De novo design of protein structure and function with RFdiffusion},
  year    = {2023},
  volume  = {620},
  pages   = {1089--1100},
  doi     = {10.1038/s41586-023-06415-8}
}

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. For 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.