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What is NVIDIA Confidential Computing?

NVIDIA Confidential Computing provides robust protection for data during active processing, ensuring that AI models and workloads are secure while executing by leveraging hardware-based trusted execution environments found in NVIDIA Hopper and Blackwell architectures, along with compatible systems. This cutting-edge technology enables businesses to conduct AI training and inference effortlessly, whether it’s on-premises, in the cloud, or at edge sites, without the need for alterations to the model's code, all while safeguarding the confidentiality and integrity of their data and models. Key features include a zero-trust isolation mechanism that effectively separates workloads from the host operating system or hypervisor, device attestation that ensures only authorized NVIDIA hardware is executing the tasks, and extensive compatibility with shared or remote infrastructures, making it suitable for independent software vendors, enterprises, and multi-tenant environments. By securing sensitive AI models, inputs, weights, and inference operations, NVIDIA Confidential Computing allows for the execution of high-performance AI applications without compromising on security or efficiency. This capability not only enhances operational performance but also empowers organizations to confidently pursue innovation, with the assurance that their proprietary information will remain protected throughout all stages of the operational lifecycle. As a result, businesses can focus on advancing their AI strategies without the constant worry of potential security breaches.

What is Fortanix Confidential AI?

Fortanix Confidential AI offers an all-encompassing platform designed for data teams to manage sensitive datasets and implement AI/ML models solely within secure computing environments, merging managed infrastructure, software, and workflow orchestration to ensure privacy compliance for organizations. This service is powered by on-demand infrastructure utilizing the high-performance Intel Ice Lake third-generation scalable Xeon processors, which allows for the execution of AI frameworks in Intel SGX and other enclave technologies, guaranteeing that no external visibility is present. Additionally, it provides hardware-backed execution proofs and detailed audit logs to satisfy strict regulatory requirements, protecting every stage of the MLOps pipeline, from data ingestion via Amazon S3 connectors or local uploads to model training, inference, and fine-tuning, while maintaining compatibility with various models. By adopting this platform, organizations can markedly improve their capability to handle sensitive information securely and foster the progression of their AI endeavors. This comprehensive solution not only enhances operational efficiency but also builds trust by ensuring the integrity and confidentiality of the data throughout its lifecycle.

Media

Media

Integrations Supported

Azure Confidential Computing
Blackwell Security
Google Cloud Platform

Integrations Supported

Amazon S3
Azure Kubernetes Service (AKS)
Microsoft Azure

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided
Free Trial Offered?

Supported Platforms

SaaS
On-Prem

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

NVIDIA

Date Founded

1993

Company Location

United States

Company Website

www.nvidia.com/en-us/data-center/solutions/confidential-computing/

Company Facts

Organization Name

Fortanix

Date Founded

2016

Company Location

United States

Company Website

www.fortanix.com/platform/confidential-ai

Categories and Features

AI Infrastructure

Not specified

Cloud GPU

Not specified

Confidential AI

Not specified

Categories and Features

Confidential AI

Not specified

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