Nvidia's AI Cloud Rental Revolution

Nvidia is expanding its AI services by collaborating with major tech companies, including Microsoft, Alphabet, and AT&T. The company’s new rental service, DGX Cloud, aims to give more developers access to its supercomputers. Nvidia has also launched AI Foundations to help companies train their customized AI models and is working with stock image database owners to avoid legal issues surrounding AI-generated content. In addition, Nvidia has announced technology to speed up the design and manufacturing of semiconductors, using its chips to reduce design calculation times from two weeks to overnight. Hans Mosesmann, a semiconductors analyst at Rosenblatt Securities, claims that Nvidia’s leadership on the software side of AI is “monumental” and that its AI products are “years ahead of the competition.”

Nvidia to Make AI Cloud Rental Available to Businesses

Nvidia Corp, based in Santa Clara, California, is the leading provider of artificial intelligence chips, with Microsoft Corp among its key partners. The company’s technology has helped build large systems for the likes of OpenAI, which created ChatGPT to answer questions and generate images from prompts.

CEO Jensen Huang has announced plans to offer access to supercomputers that are used to develop AI technologies to a wider range of businesses. Although the rental fees are steep at $37,000 per month for eight of Nvidia’s flagship A100 or H100 chips strung together, offering this service to more customers could boost the AI sector. Nvidia’s shares have risen by 77% in 2021, making it five times more valuable than its rival Intel Corp.

During Nvidia’s annual software developer conference, Huang revealed that the company is working with Oracle Corp and other partners to offer access to Nvidia’s DGX supercomputers, which can contain up to 32,000 of Nvidia’s chips. The service will be accessible via a web browser.

By offering AI cloud rental to a wider customer base, Nvidia aims to increase the use of its technology and fuel the development of AI-powered applications across different sectors.

Nvidia Collaborates with Major Tech Companies to Expand AI Services

During Nvidia’s virtual keynote address, CEO Jensen Huang compared the company’s expansion of AI services to Apple Inc’s opening up of the market for smartphones. Nvidia is partnering with Microsoft and Alphabet Inc to offer its supercomputers, which are used to create new AI products, as a service. In addition, Nvidia has launched new chips and software to make chatbots and other AI products more affordable to operate on a day-to-day basis after they have been created using supercomputers. Analysts claim that Nvidia’s AI products are “years ahead of the competition” and that the company’s leadership on the software side of AI is “monumental.”

Nvidia is collaborating with several major tech companies to accelerate the development and use of AI technology. The company is working with AT&T to improve the efficiency of truck dispatching, collaborating with quantum computing researchers to speed up software development, and partnering with Taiwan Semiconductor Manufacturing Co to accelerate chip development. Nvidia has also launched a new rental service, DGX Cloud, which could give many more developers access to tens of thousands of its chips at once. Biotech firm Amgen Inc and software firm ServiceNow Inc have already started using the service.

Nvidia’s new AI Foundations service helps companies train their customized AI models, and major owners of stock image databases plan to use the service to avoid legal questions about copyright of images used to generate AI content. In addition, Nvidia has announced technology to speed up the design and manufacturing of semiconductors. The company’s software uses Nvidia’s chips to reduce the time it takes to complete chip design calculations from two weeks to overnight, while reducing the electricity used in the task from 35 megawatts to 5 megawatts. Nvidia is working with ASML Holding, Synopsys Inc and TSMC to bring this technology to market, with TSMC starting to prepare the technology for production in June 2022.

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