Nvidia announced the availability of Nvidia AI Enterprise, a comprehensive software suite of AI tools and frameworks that enables the hundreds of thousands of companies running VMware vSphere to virtualize AI workloads on Nvidia-Certified Systems.
Leading manufacturers Atos, Dell Technologies, GIGABYTE, Hewlett Packard Enterprise, Inspur, Lenovo and Supermicro are offering Nvidia-Certified Systems optimised for AI workloads on VMware vSphere with Nvidia AI Enterprise. Separately, Dell Technologies announced Dell EMC VxRail as the first hyper-converged platform to be qualified as an NvidiaCertified System for Nvidia AI Enterprise.
To help teams of data scientists run their AI workloads most efficiently, Domino Data Lab today announced it is validating its Domino Enterprise MLOps Platform with Nvidia AI Enterprise, which runs on mainstream Nvidia-Certified Systems.
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“The first wave of AI has been powered by specialised infrastructure that focused adoption on industry pioneers,” said Manuvir Das, head of Enterprise Computing at Nvidia. “Today is the beginning of a new chapter in the age of AI, as Nvidia software brings its transformative power within reach for enterprises around the world that run their workloads on VMware with mainstream data center servers.”
“As AI applications become critical, customers want to run them on their enterprise infrastructure for manageability, scalability, security and governance,” said Krish Prasad, senior vice president and general manager of the Cloud Platform Business Unit at VMware. “Running Nvidia AI Enterprise on VMware vSphere delivers a certified, end-to-end AI-Ready enterprise platform that’s easy to deploy and operate.”
“Partnering closely with Nvidia, we’re deepening our product integrations by enabling the Domino Enterprise MLOps Platform to run with a broader range of NVIDIA GPUs and validating it for Nvidia AI Enterprise,” said Nick Elprin, CEO and co-founder of Domino Data Lab. “This new offering will help hundreds of thousands of enterprises accelerate data science at scale.”
