VMware joins hand with Nvidia on AI-powered hybrid cloud platform
By MYBRANDBOOK
VMWare has announced of partnering with Nvidia. The company has said that its Cloud Foundation (VCF) 4.4 platform is now generally available for enterprises, and it contains Nvidia’s own AI Enterprise Suite. This consists of pre-built sets of AI software, pre-trained models, and other tools that data scientists can use to create and deploy next-gen apps.
This is all to be deployed in a full-stack, hyper-converged infrastructure as a hybrid cloud platform, currently the most popular option in the world of cloud computing.
With this integration of platforms, VMware claims that VCF 4.4 brings improved AI and ML workload support and the capability to share virtual GPU’s — a relatively recent advancement — across multiple instances.
The two companies have been working as partners for more than two years, and this cohabitation in a single platform is the most important result to date of that business deal.
Additional notable new features in VCF 4.4 for developers include the following:
· Flexible vRealize product upgrades
· Upgrade precheck improvements
· Disabling SSH on ESXi hosts
· User activity logging
· Multiple security fixes
In VCF 4.4, the integration of the Nvidia’s and VMware’s software suites makes it possible to deliver Kubernetes clusters to AI developer teams at a rapid pace while still enabling enterprise-class governance, reliability, and security within VMware vSphere with Tanzu environments. For enterprises implementing AI/ML (machine learning) infrastructure, the combination enables business outcomes without creating friction between IT operations and lines of business.
Rick Walsworth, Group Manager for the VMware Cloud Foundation Product Marketing team, wrote in a blog post, “The challenge for many organizations architecting AI infrastructure into their overall technology stack is the complexity associated with it. Traditionally, AI technology hasn’t integrated seamlessly into existing IT stacks and operational processes. This is compounded by the fact that data scientists typically want to experiment quickly, configuring and reconfiguring clusters to get to their desired outcome.”
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