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Run:ai’s Atlas delivers simple, on-demand access to compute resources to AI Practitioners while giving Engineering teams peace- of-mind. Atlas assures GPU resources are constantly optimized, accessed, and utilized to their fullest on any infrastructure architecture and size.
Run:ai Atlas offers dashboards and analytics giving IT insight across all resources and workloads. Align resource allocation to business goals by setting policies and priorities across departments, projects or users.
Dynamically allocate resources using a Smart Scheduler built as a simple Kubernetes plug-in, built from the ground up to work with containers and cloud-native architectures.
Advanced technology for sharing and optimizing GPUs improves utilization by more than 2X, and significantly increases ROI on existing GPU and CPU infrastructure.
AI practitioners can easily consume resources in a self-service model using native Run:ai workflows to build, train and deploy models; or by using 3rd party integrations, such as MLflow, Kubeflow and more.