Make scarce GPU resources accessible and fully utilized for all AI teams across your organization.Get a Demo
Run:ai Atlas helps Centralize AI infrastructures and bring teams together into one platform that allocates, efficiently-distributes, and simplifies Deep Learning teams’ access to AI compute resources.
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 and users.
Run:ai’s Smart Scheduler dynamically allocates AI resources such as GPUs. Built as a simple Kubernetes plug-in Run:ai Atlas is designed to work with containers and cloud-native architectures.
Run:ai’s Atlas removes the complexity of the underlying infrastructure and democratizes the access to AI compute resources. Now AI practitioners can focus more on research work and iterate faster.
Run:ai Atlas workflows are optimized to support the full AI development lifecycle, by allowing AI teams to use the AI stack of their choice,including any party MLOps tools.
Build your AI infrastructure with cloud-like resource accessibility and management, on any infrastructure, and enable researchers to use any ML and DS tools they choose.
Run:AI’s platform builds off of powerful distributed computing and scheduling concepts from High Performance Computing (HPC), but is implemented as a simple Kubernetes plugin.
Our AI Cloud Platform speeds up data science workflows and creates visibility for IT teams who can now manage valuable resources more efficiently, and ultimately reduce idle GPU time.
Simplify management of GPU allocation and ensure sharing between users, teams, and projects according to business policies & goals.
Run:AI brings a cloud-like experience to resource management wherever your resources are: cloud, on-premises or hybrid cloud.
Super-powerful scheduling built as a simple Kubernetes plug-in, built from the ground up to work with containers and cloud-native architectures.
Make use of fractional GPUs, integer GPUs, and multiple-nodes of GPUs, for distributed training on Kubernetes.
Improves utilization by more than 2X, and significantly increases ROI on existing GPU and CPU infrastructure.
Run workloads on fractions of GPUs, converting spare capacity to speed and increasing infrastructure efficiency.