Radium looks to speed up AI and ML jobs in cloud datacenters

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Today, Radium, a startup that aims to use artificial intelligence and machine learning to gain more computing power from cloud hardware, announced that it is abandoning Stealth Mode But is using its solutions. Area and Silicon Valley.

The main product, called the launchpad, allows users to start and stop projects on bare metal machines, eliminating the extra layers of hypervisors and virtualization software. Radium offered benchmark tests on machine learning jobs with speeds of up to 30% and up to 140%.

“Our initial testing shows that bare metal servers provide a good cloud computing platform for high-performance deep learning and projected workloads required for such applications,” said Srinivas Narasimhan, a professor at Carnegie Mellons School of Computer Science who tested the product. To work with the company.

Improve the performance of the hypervisor

Many cloud products rely heavily on virtualization software layers or “hypervisors” that allow a physical machine to simulate a variety of smaller machines that appear independent to users. But there is a cost to implementing a hypervisor. Simulation often adds a slight delay to tasks such as retrieving data from a disk or sending data packets over the Internet.

[ Related: The emergence of datacenter-as-a-service ]

Adam Handin, CEO and co-founder of Radium, said, “At a higher level, we felt that the dim nature of the middleware and hypervisor stacks in the architecture of leading cloud providers could be improved.”

Radium’s fairly metal approach has been optimized to target the marketplace for AI in research labs and corporate stacks. The smart deployment software built into the launchpad is designed to help optimize performance for AI algorithms running inside.

There is also the cost of production in view of the general data-heavy workload in the field. They do not add any additional fees for entry and exit, a set of often overlooked fees which is sometimes a source of big surprise for some other cloud users.

Listening to the ML community

“We designed and built the cloud platform to meet the needs of the most demanding AI workload,” explained Handy. “Public cloud with the benefits of the private cloud. We’re also listening to the ML community who are frustrated with non-portable software, as well as the hefty data aggression fees at leading cloud providers that attract customers. “

Other companies are experimenting with using AI algorithms to allocate resources in public clouds. Zesti, for example, offers a product that will monitor instances in a public cloud like AWS and make decisions to save money by shrinking or shutting down over-provisioning machines.

[ Related: Cloud datacenters anticipated to become largely robot-dependent by 2025 ]

Cyxtera already operates 61 datacenters in 21 markets worldwide. The company specializes in collocation and bare metal servers primarily for the enterprise market, but is expanding to provide services such as AI / ML training using Nvidia machines.

Deploying radium in the datacenter

“Syxtera’s global platform and scalable interconnection solutions provide a reliable foundation for Radium and its customers to grow their businesses as their workload scale,” said Holland Berry, senior vice president and field CTO for Syxtera.

Cyxtera has deployed the current version of the Radium launchpad at three centers so far and plans to expand soon.

“Radium is breaking the mold of cloud automation with its launchpad ecosystem, enabling simple processes for provisioning, configuration and deployment,” Berry said.


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