NVIDIA releases AI Enterprise Suite 1.1
NVIDIA’s AI Enterprise software suite is now generally available. The version 1.1 brings new updates including production support for containerized AI with the NVIDIA software on VMware vSphere with Tanzu. Enterprises can now run accelerated AI workloads on vSphere, running in both Kubernetes containers and virtual machines with NVIDIA AI Enterprise to support advanced AI development on mainstream IT infrastructure. NVIDIA will soon add VMware vSphere with Tanzu support to the NVIDIA LaunchPad program for NVIDIA AI Enterprise, available at nine Equinix locations around the world. Qualified enterprises can test and prototype AI workloads at no charge through curated labs designed for AI practitioners and IT admins. “Organisations are accelerating AI and ML development projects and VMware vSphere with Tanzu running NVIDIA AI Enterprise easily empowers AI development requirements with modern infrastructure services,” said Matt Morgan, VP of Product Marketing, Cloud Infrastructure Business Group at VMware. “This announcement marks another key milestone for VMware and NVIDIA in our sustained efforts to help teams leverage AI across the enterprise.” The 1.1 AI Enterprise also provides validation for the Domino Data Lab Enterprise MLOps Platform with VMware vSphere with Tanzu. The new integration enables more companies to cost-effectively scale data science by accelerating research, model development, and model deployment on mainstream accelerated servers. Enterprises are open to using containerized development for AI, but the complexity of these workloads requires orchestration across many layers of infrastructure. “AI is a very popular modern workload that is increasingly favoring deployment in containers. However, deploying AI capabilities at scale within the enterprise can be extremely complex, requiring enablement at multiple layers of the stack, from AI software frameworks, operating systems, containers, VMs, and down to the hardware,” said Gary Chen, research director, Software Defined Compute at IDC. “Turnkey, full-stack AI solutions can greatly simplify deployment and make AI more accessible within the enterprise.” Source: NVIDIA NVIDIA AI Enterprise release is in lock step with the launch of Cisco UCS C240 M6 rack server with NVIDIA A100 Tensor Core GPUs. The two-socket, 2RU form factor can power a wide range of storage and I/O-intensive applications, such as big data analytics, databases, collaboration, virtualization, consolidation and high-performance computing. Hitachi has also developed an NVIDIA-Certified System Hitachi Vantara–compatible with NVIDIA AI Enterprise. The general-purpose, dual-processor server is optimised for performance and capacity, and delivers a balance of compute and storage with the flexibility to power a wide range of solutions and applications.
Enterprises are open to using containerized development for AI, but the complexity of these workloads requires orchestration across many layers of infrastructure.
Enterprise are open to using containerized development for AI, but the complexity of these workloads requires orchestration.
["enterprise ai", "NVIDIA", "VMWare"]
["big data", "VMWare", "data science", "AI", "ML", "MLOps", "RAG", "GAN", "analytics", "enterprise ai", "NVIDIA", "R", "kubernetes"]
["AI", "ML", "data science", "analytics", "MLOps", "RAG", "kubernetes", "R", "big data", "GAN"]
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