Need to build an AI platform for many users, a modular AI toolstack and automated GPU resource management?
Multiple silos of AI & GPU infrastructure? How to get the most out of these resources?
Want to take your current solution to the next level and deploy models with a modern cloud native AI infrastructure and optimized GPU utilization?
Get frustrated when you chase after available GPUs for your experiments? Tired of seeing “CUDA out of memory”?
The Run:ai Atlas platform gathers all compute resources in a centralized pool regardless of their location (on- premises or in the cloud) and with our Kubernetes-based smart workload scheduler assures dynamic allocation of resources.
Integration to NVIDIA AI stack provides sophisticated sharing and GPU fractioning across multiple workloads and for optimized utilization.
Through a centralized control plane, IT Orgs gain full control and visibility over all resources, workloads and users.
With Atlas, AI practitioners have self-service, dynamic access to compute power to support their changing needs straight within their ML tool-of-choice.