MIAMI — Cyxtera launched its landmark AI/ML compute-as-a-service offering to deliver data center platform capabilities enabling AI-powered workloads. Leveraging the NVIDIA DGX™ A100 system, this new offering provides enterprises greater agility and faster deployment with a flexible OpEx model, avoiding the need for large capital outlays and over provisioning.
Cyxtera's solution offers the simplicity and ease of the cloud with the deterministic performance of dedicated infrastructure. This new, flexible infrastructure model leverages point-and-click provisioning via the company’s data center services exchange. Customers will also have direct access to an ecosystem of service providers and technology solutions, including storage/storage as a service (StaaS), interconnectivity, and security, among other managed services.
“Launching our DGX-based AI/ML compute-as-a-service offering reinforces our continued focus on providing enterprise customers with a rich variety of leading-edge infrastructure options across our global footprint,” said Russell Cozart, senior vice president of marketing and product strategy for Cyxtera. “As a certified NVIDIA DGX-Ready Program partner, we’re offering accelerated solutions ideally suited to our enterprise customer base.”
Enterprises will be able to provision NVIDIA DGX A100 systems via Cyxtera’s service exchange, providing the security and control of single-tenant, dedicated infrastructure combined with the flexibility and agility of cloud. The NVIDIA DGX A100 offering also allows managed service providers to focus on service delivery by eliminating large, upfront capital investments in data center infrastructure while providing access to customers via the Cyxtera exchange.
“Many organizations want to speed up their AI innovation cycle but lack the infrastructure to support it,” said Charlie Boyle, vice president and general manager of DGX Systems at NVIDIA. “Cyxtera’s AI/ML compute-as-a-service offering makes it easy to leverage the performance, flexibility, and efficiency of DGX A100 in a cost-effective model, enabling more businesses to succeed in the race to deploy AI.”