Top 5 Platforms to Run NVIDIA BioNeMo

NVIDIA’s BioNeMo framework serves researchers in their challenges across computational biology and drug discovery research and development. Teams can predict molecular properties, enhance protein engineering, and streamline the drug development process for faster and more accurate scientific discoveries.

The table below displays the different models included in the BioNeMo framework featuring: DNABERT, OpenFold, MolMIM, EquiDock, DiffDock, ESM-2nv, ESM-1nv, ProtT5, MegaMolBART, and Geneformer. Learn how to integrate them into your own BioNeMo workflow here.

(Image source: NVIDIA NIM Documentation)

Choosing the right platform to run NVIDIA BioNeMo can significantly impact the success of your bioinformatics projects. Here’s a look at the top five platforms that have embedded NVIDIA BioNeMo into their tool stack.

1. Saturn Cloud

Saturn Cloud is a platform for data scientists and ML engineers who need a powerful, scalable, and easy-to-use cloud infrastructure. With NVIDIA BioNeMo integrated, users can leverage NVIDIA’s AI capabilities without complex setups and utilize them across their preferred cloud solutions (AWS, Azure, Coreweave, etc). Key highlights include:

• Multi-Cloud: Runs on any cloud, including AWS, Azure, GCP, and more

• High Performance: Access to NVIDIA GPUs ensures rapid processing and analysis.

• Ease of Use: User-friendly interface simplifies deployment and management of AI projects.

• Collaboration: Teams can collaborate and easily share work by creating groups.

• Scalability: Seamless scaling from individual projects to enterprise-level applications.

• Fast Support: Excellent customer service to help you make the most of NVIDIA’s tools.

✅   Best For:  

Data scientists and bioinformatics researchers seeking a streamlined and efficient cloud platform for their AI projects.

🛠️   Setup Complexity:  

Low, minimal effort required.

2. AWS (Amazon Web Services) SageMaker

AWS provides infrastructure and a vast array of services, including embedding NVIDIA’s AI tools into their offerings. Highlights include:

• Global Infrastructure: Extensive network of data centers ensures low latency and high availability.

• Flexibility: Wide range of instance types, including those optimized for GPU workloads.

• Integration: Seamless integration with other AWS services for a holistic cloud experience.

• AWS-Only: Works only within the AWS ecosystem, excluding other clouds such as Azure, OCI, GCP, etc.

✅   Best For:  

Organizations needing global reach and flexibility in their bioinformatics and AI projects.

🛠️   Setup Complexity:  

Moderate, some setup and management required.

3. Vertex AI

Vertex AI offers an environment for developing and deploying AI models. With the integration of NVIDIA BioNeMo, users can now access its capabilities for bioinformatics applications. Highlights include:

• Advanced AI Tools: Access Google’s AI and ML services alongside NVIDIA’s tools.

• Scalable Infrastructure: Designed to handle workloads of any size, from small research projects to large-scale deployments.

• Collaboration: Tools like Google Colab and AI Hub facilitate team collaboration and sharing of models and data.

• Vertex-Only: Works only within the Vertex/Google ecosystem, excluding other clouds such as AWS, OCI, Azure, etc.

✅   Best For:  

Teams looking for an integrated environment that supports collaboration and scalability.

🛠️   Setup Complexity:  

Low, user-friendly tools and services reduce the need for extensive setup.

4. Microsoft Azure ML Studio

Azure’s cloud platform supports NVIDIA’s latest AI tools for bioinformatics research. Azure’s key highlights include:

• Enterprise Integration: Integration with existing Microsoft products like Office 365 and Azure DevOps.

• Security: Security features and compliance certifications suitable for sensitive healthcare data.

• Performance: High-performance computing capabilities with NVIDIA GPUs for intensive AI tasks.

• Azure-Only: Works only within the Azure ecosystem, excluding other clouds such as AWS, OCI, etc.

✅   Best For:  

Enterprises needing secure, high-performance cloud solutions with smooth Microsoft ecosystem integration.

🛠️   Setup Complexity:  

Moderate, some setup and management required but streamlined with Microsoft’s tools.

5. Oracle Cloud

Oracle Cloud offers an environment optimized for enterprise workloads, including those involving NVIDIA BioNeMo. Key benefits include:

• Enterprise Optimization: Designed to handle large-scale enterprise applications with ease.

• High Performance: Access to NVIDIA GPUs for accelerated AI and ML workloads.

• Security and Compliance: Offers security features and compliance certifications suitable for sensitive data.

• Oracle-Only: Works only within the Oracle ecosystem, excluding other clouds such as AWS, Azure, Google, etc.

✅   Best For:  

Large enterprises requiring high-performance computing and secure, compliant cloud solutions.

🛠️   Setup Complexity:  

Moderate, some setup and management required, but well-supported by Oracle’s tools.

Other MLOps Platforms

Other AI and MLOps platforms including Amazon SageMaker, Dataiku, DataRobot, deepset, Domino Data Lab, LangChain, Llama Index, Replicate, Run.ai, and more have also embedded BioNeMo into their platforms, however, may require additional DevOps support and configuration.

Choosing the Right Platform

Saturn Cloud stands out for its user-friendly interface, scalability, and excellent support, making it a top choice for many researchers. However, platforms like AWS, Google Cloud, Microsoft Azure, and OCI offer unique advantages, depending on your needs and existing infrastructure. Evaluate these options to find the best fit for your AI-driven bioinformatics research.

By leveraging these platforms, you can drive faster and more accurate discoveries in bioinformatics. Whether you prioritize ease of use, global reach, or dedicated AI infrastructure, there’s a platform tailored to your needs.

For more information, check out the NVIDIA BioNeMo Framework documentation.

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