Organizations are increasingly adopting hybrid and multi-cloud strategies to access the latest compute resources, consistently support worldwide customers, and optimize cost. However…

NVIDIA offers a consistent, full stack to develop on a GPU-powered on-premises or on-cloud instance. You can then deploy that AI application on any GPU-powered platform without code changes.

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    1 年前

    TL;DR: (AI-generated 🤖)

    Organizations are increasingly adopting hybrid and multi-cloud strategies to optimize cost and access the latest compute resources, but one challenge they face is operationalizing AI applications across different platforms. NVIDIA offers a solution with its Cloud Native Stack Virtual Machine Image (VMI), which is GPU-accelerated and pre-installed with Cloud Native Stack and the NVIDIA GPU Operator. The GPU Operator automates the lifecycle management of software required to expose GPUs on Kubernetes, improving performance and utilization. Run:ai, a compute orchestration platform for AI workloads, has certified NVIDIA AI Enterprise on its Atlas platform, enabling enterprises to streamline the development and deployment of AI models. To simplify the process, NVIDIA offers enterprise support for their VMI and GPU Operator through the purchase of NVIDIA AI Enterprise, which provides access to AI experts, service-level agreements, and upgrade and maintenance control.

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