NVIDIA and Run:ai provide a consistent full-stack solution that enables developers to build and test AI applications on GPU-powered on-premises or on-cloud instances.
Once you have developed and validated your AI application on a GPU-powered NVIDIA platform, you can deploy it to other GPU-powered platforms without requiring extensive code changes. This flexibility enables organizations to seamlessly deploy AI applications across hybrid and multi-cloud environments, saving time and effort while maintaining consistent performance.
NVIDIA recognizes that changes in the technology stack present MLOps teams and developers with the complexity of adapting AI applications to run seamlessly across different target platforms. NVIDIA enables organizations to harness the full potential of AI without the burden of large code changes.
Run:ai, an industry leader in computing orchestration for AI workloads, has certified NVIDIA AI Enterprise, an end-to-end secure, cloud-native AI software suite on the Atlas platform.
Run:ai Atlas includes GPU orchestration capabilities that allow researchers to use GPUs more efficiently. It does this by automating the orchestration of AI workloads and the management and virtualization of hardware resources across teams and clusters.
Run:ai can be installed on any Kubernetes cluster and provides efficient scheduling and monitoring capabilities for your AI infrastructure. With NVIDIA Cloud Native Stack VMI, you can add cloud instances to your Kubernetes cluster to become worker nodes that take advantage of the cluster’s GPUs.
Customers can purchase NVIDIA AI Enterprise through NVIDIA partners and receive enterprise support for NVIDIA VMI and GPU Operator.
