NVIDIA Run:ai
بواسطة NVIDIA Corporation
Kubernetes-native platform for orchestrating AI workloads and optimizing GPU resources
NVIDIA Run:ai is available only via private offer. Please contact your NVIDIA sales representative or visit this link for details.
NVIDIA Run:ai delivers an enterprise-grade AI workload orchestration platform that maximizes the efficiency and scalability of your Azure GPU infrastructure. Purpose-built for Kubernetes environments and optimized for AI/ML workloads, Run:ai enables Azure customers to achieve greater throughput, improved utilization, and faster model development - all while maintaining tight control over resources and costs.
Run:ai abstracts the complexity of managing GPU resources and accelerates time-to-insight for data science teams, while providing DevOps and IT stakeholders with robust tools for visibility, policy enforcement, and cost optimization. Run:ai ensures rapid deployment and integration with Azure-native services such as Azure AKS and Azure VM GPU instances.
Key capabilities:
Flexible GPU Scaling for AI Workloads: Seamlessly scale GPU resources up or down across Azure environments to match the dynamic needs of training, tuning, and inference.
Automated GPU Orchestration: Ensure optimal resource allocation and scheduling for multiple workloads using intelligent policies that minimize idle time.
Team-Based Resource Governance: Use role-based access control and team-level quotas to ensure isolation, compliance, and shared infrastructure visibility across AI teams.
Deploy seamlessly alongside Azure Kubernetes Service (AKS) and integrate with Azure-native services such as Azure Machine Learning, Azure Monitor, and Azure VM GPU instances to deliver a unified and consistent operational experience.
MLOps Workflow Compatibility: Native support for JupyterHub, Kubeflow, MLflow, and other AWS-hosted tools to support end-to-end machine learning pipelines.
With NVIDIA Run:ai, organizations can rapidly onboard AI teams, democratize access to GPU infrastructure, and accelerate innovation while keeping infrastructure flexible and cost-effective. The solution is ideal for enterprises looking to scale AI initiatives without the burden of managing complex infrastructure manually.