The worldwide adoption of generative AI has driven massive demand for accelerated compute hardware globally. In enterprises, this has accelerated the deployment of accelerated private cloud infrastructure. At the regional level, this demand for compute infrastructure has given rise to a new category of cloud providers who offer accelerated compute (GPU) capacity for AI workloads, also known as GPU…
]]>Editor’s note: Interested in GPU Operator? Register for our upcoming webinar on January 20th, “How to Easily use GPUs with Kubernetes”. NVIDIA GPU Operator allows organizations to easily scale NVIDIA GPUs on Kubernetes. By simplifying the deployment and management of GPUs with Kubernetes, the GPU Operator enables infrastructure teams to scale GPU applications error-free, within minutes…
]]>Editor’s note: Interested in GPU Operator? Register for our upcoming webinar on January 20th, “How to Easily use GPUs with Kubernetes”. In the last post, we looked at how the GPU Operator has evolved, adding a rich feature set to handle GPU discovery, support for the new Multi-Instance GPU (MIG) capability of the NVIDIA Ampere Architecture, vGPU, and certification for use with Red Hat OpenShift.
]]>Editor’s note: Interested in GPU Operator? Register for our upcoming webinar on January 20th, “How to Easily use GPUs with Kubernetes”. Reliably provisioning servers with GPUs in Kubernetes can quickly become complex as multiple components must be installed and managed to use GPUs. The GPU Operator, based on the Operator Framework, simplifies the initial deployment and management of GPU…
]]>Editor’s note: Interested in GPU Operator? Register for our upcoming webinar on January 20th, “How to Easily use GPUs with Kubernetes”. Reliably provisioning servers with GPUs can quickly become complex as multiple components must be installed and managed to use GPUs with Kubernetes. The GPU Operator simplifies the initial deployment and management and is based on the Operator Framework.
]]>Modern expectations for agile capabilities and constant innovation—with zero downtime—calls for a change in how software for embedded and edge devices are developed and deployed. Adopting cloud-native paradigms like microservices, containerization, and container orchestration at the edge is the way forward but complexity of deployment, management, and security concerns gets in the way of scaling.
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