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NVIDIA


NVIDIA GPU-powered instances on Google Cloud provide an optimal platform for organizations to develop their AI applications on the latest hardware and software stack, then seamlessly deploy those applications at scale in production. Simplifying Workflows to Speedup AI Developments NVIDIA recently announced the One Click Deploy feature on the NVIDIA NGC catalog, the hub for …

Rackspace Technology® (NASDAQ: RXT), a leading end-to-end, multicloud technology solutions company, announced that it has joined NVIDIA’s DGX-Ready Managed Services program. The program certifies leading technology partners who are eligible to support enterprises that leverage NVIDIA DGX™ systems. As part of the relationship with NVIDIA, Rackspace Technology has also joined the NVIDIA Partner Network (NPN). NVIDIA …

While AI workloads are becoming more pervasive, challenges with deploying AI have slowed adoption. Blockers like data complexity, data silos, and lack of infrastructure contribute to the difficulty of deploying AI workloads, and to address these issues, organizations need an integrated, scalable, and high-performing solution. VMware Tanzu Kubernetes Grid, is a multi-cloud Kubernetes footprint that …

Supercomputers are high-level computers used for managing very large databases or great amounts of computation. Tom Merritt lists the top five ranked supercomputers in the world, according to TOP500. HPE is building a supercomputer called LUMI in Finland, and it is expected to have peak performance of more than 550 petaflops, putting it at the top …

Oracle announced that it is the first major cloud provider to make NVIDIA A100 Tensor Core GPU on bare metal instances generally available. Oracle’s latest GPU instances enable customers in industries such as automotive and aerospace to run complex, data-intensive, high-performance applications like modeling and simulations more efficiently and at a lower cost than ever …

Enterprises currently face the challenge of how to adopt and integrate AI and ML into their operations effectively, at scale and with minimum complexity. In tandem, today’s AI workloads have become increasingly advanced and the compute power required to support them has exponentially increased. Canonical and NVIDIA have collaborated to help enterprises accelerate their adoption …