Run:AI Under the Hood
Build and Train Models with Unlimited Compute
Introducing Run:AI
The Run:AI software platform decouples data science workloads from the underlying hardware. By pooling resources and applying an advanced scheduling mechanism to data science workflows, Run:AI greatly increases the ability of data science teams to fully utilize all available resources, essentially creating unlimited compute. Data scientists can increase the number of experiments they run, speed time to results, and ultimately meet the business goals of their AI initiatives. IT gains control and visibility over the full AI infrastructure stack.

From Static Allocations to Guaranteed Quotas
Run:AI’s virtualization software builds off of powerful distributed computing and scheduling concepts from High Performance Computing (HPC), but is implemented as a simple Kubernetes plugin. The product speeds up data science workflows and creates visibility for IT teams who can now manage expensive resources more efficiently, and ultimately reduce idle GPU time.
Pool GPU Compute
Pool GPU compute resources to ensure visibility and control over prioritization and allocation of resources
Guaranteed Quotas
Automatic and dynamic provisioning of GPUs to break the limitations of static allocations
Elasticity
Dynamically change the number of resources allocated to a job to accelerate data science delivery and increase GPU utilization
Kubernetes-based Scheduler
Easily orchestrate distributed training with batch scheduling, gang scheduling and topology awareness
Gradient Accumulation
Why Virtualize AI
Decouple data science from hardware
Speed Data Science Workflows
Never hit compute or memory bottlenecks again
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