VMware institutionalized IT virtualization 20 years ago and changed forever the way enterprise systems operate by making workloads more easily movable and controllable. Now the Palo Alto, California.-based giant – reports estimate that 95% of all IT departments have at least one VMware product – is moving into a new realm of AI-powered development of next-generation infrastructure.
To make the move, VMWare is partnering with Nvidia, the hottest IT processor maker on earth with 80% of the graphical processing unit (GPU) market. Nividia has been the most successful chipmaker to deploy AI in silicon, due mostly to its long history in making chips for gaming.
VMware revealed last week 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.
2-year VMware-Nvidia partnership bears fruit
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
“The challenge for many organizations architecting AI infrastructure into their overall technology stack is the complexity associated with it,” Rick Walsworth, group manager for the VMware Cloud Foundation Product Marketing team, wrote in a blog post. “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.
“There is also business pressure to move these initiatives rapidly along the AI lifecycle, with a goal of scaling AI workloads into production-ready environments as quickly and seamlessly as possible.”
Delivering Kubernetes clusters to AI teams
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, Walsworth said.
“The reason this solution is so powerful is that it combines the strengths of both companies,” Walsworth said. “VMware brings deep knowledge of infrastructure, operations, and management, and Nvidia delivers accelerated computing expertise, GPU virtualization technology, and prebuilt sets of AI software, pre-trained models, and other tools that data scientists can use to create applications and quickly deploy them. “The Nvidia AI Enterprise suite includes frameworks and tools like PyTorch, TensorFlow, TensorRT, Nvidia RAPIDS, and Nvidia Triton Inference Server, all packaged up as containers that can be easily deployed and managed.”
