JFrog Partners with NVIDIA to Accelerate Agentic AI, Integrating the JFrog Platform with NVIDIA Enterprise AI Factory
New Full-Stack, Validated Design Aims to Accelerate AI/ML Model Engineering, Security, Operations and Delivery for the AI-powered Enterprise
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The JFrog Platform will serve as the cornerstone software artifact repository and secure model registry for the new
Following a successful NVIDIA NIM integration with the JFrog Platform, this new collaboration delivers a full-spectrum MLOps solution, designed to ensure scalable, secure and seamless deployment of AI-powered applications using the NVIDIA Blackwell platform.
"The future of AI depends not only on innovation - but on trust, control, and seamless execution," said
Delivering Critical Infrastructure to Enable Future AI Innovation
The JFrog Platform provides customers with a “single source of truth” for software components within
- Secure & Governed Software Component Visibility: Enables all ML models, engines, and software artifacts to be scanned for security issues, versioned, governed, and traceable across the entire software development lifecycle.
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End-to-End Software Artifact & ML Model Management: Enables the seamless pulling, uploading, and hosting of AI models and datasets, AI containers, Docker containers, and dependencies optimized for the
NVIDIA Enterprise AI Factory validated design. - Rapid, Trusted AI/ML Application Provisioning in Runtime: Simplifies configuration of AI environments by eliminating the need for runtime environments to pull components from outside of the organization, thanks to the universality, proven scalability and robustness of JFrog Artifactory.
- Future-proofed for Evolving GenAI Applications: Quickly and easily manages ML model versioning and upgrades to new and approved model generations.
“Enterprises building AI factories need to manage the complexity of AI adoption while ensuring performance, governance and trust,” said
The integration is designed to enable the JFrog Platform to run natively on NVIDIA Blackwell systems to help reduce latency and process tasks with unparalleled performance, efficiency, and scale. It supports a wide range of AI-enabled enterprise applications, agentic and physical AI workflows, autonomous decision-making, and real-time data analysis across various industries, including financial services, healthcare, telecommunications, retail, media, and manufacturing. Additionally, the system leverages NVIDIA’s engineering know-how and partner ecosystem to help enterprises accelerate time-to-value and mitigate the risks of AI deployment.
Those interested in learning more about JFrog and NVIDIA integrations or going hands-on with the NVIDIA NIM trial should visit https://jfrog.com/jfrog-and-nvidia/.
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About JFrog
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This press release contains “forward-looking” statements, as that term is defined under the
These forward-looking statements are based on our current assumptions, expectations, and beliefs and are subject to substantial risks, uncertainties, assumptions and changes in circumstances that may cause JFrog’s actual results, performance or achievements to differ materially from those expressed or implied in any forward-looking statement. There are a significant number of factors that could cause actual results, performance or achievements to differ materially from statements made in this press release, including but not limited to risks detailed in our filings with the
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