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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 Lium?

Lium is a rental platform that allows users to lease NVIDIA GPUs on an hourly basis, featuring a variety of providers who list their hardware, which fosters competitive pricing through market forces. Users have the option to rent a single GPU or a full 8-GPU node, with the ability to launch a container in about a minute, granting them SSH and Jupyter access. The current hourly prices are $0.16 for an RTX 3090, $0.27 for an RTX 4090, $0.49 for an RTX 5090, and $4.23 for an H200, with billing calculated on a per-second basis, meaning that a job lasting 12 minutes will only incur charges for that exact time. To aid in deployment, Lium provides a command line interface and a Python SDK, along with a complimentary public API that enables users to monitor real-time pricing and availability. New customers receive a welcome credit of $5, and there are no contracts or minimum commitments required for service usage. This platform is tailored specifically for machine learning applications such as training, fine-tuning, inference, and batch processing, particularly when conventional hyperscaler pricing structures may not be advantageous. By offering such flexible and accessible services, Lium empowers both developers and researchers to make effective use of high-performance GPU resources as necessary, ultimately enhancing productivity in computational tasks. This approach not only democratizes access to cutting-edge technology but also supports innovation in various fields reliant on machine learning.

Media

Media

No images available

Integrations Supported

Azure Confidential Computing
Blackwell Security
Google Cloud Platform

Integrations Supported

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

$0.16 per GPU per hour

Supported Platforms

SaaS
On-Prem

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training

Training Options

Documentation Hub

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

Lium

Company Website

lium.io

Categories and Features

AI Infrastructure

Not specified

Cloud GPU

Not specified

Confidential AI

Not specified

Categories and Features

Cloud GPU

Not specified

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