Run:ai Cluster Engine
Dynamically manage AI workloads and resources for optimal GPU utilization
Utilize 100% of Your Cluster with Our AI Workload Scheduler

Set priorities and quotas.
Run:ai Scheduler will continuously optimize resource allocations accordingly.

Fairshare Scheduling

Prevent resource contention with over quota priorities and automatic job preemption and fairshare resource allocation

Job Queues

Reduce GPU idleness and increase Cluster utilization with Job Queueing and opportunistic batch job scheduling

Guaranteed Quotas

Prevent GPU Hogging and guarantee access to always-available GPU quotas per user

Bin Packing & Consolidation

Optimize cluster utilization and mitigate cluster fragmentation with automatic bin packing and workload consolidation

Gang  Scheduling

Schedule distributed workloads reliably on multiple nodes

Optimize Costs with
Fractional GPUs

Increase efficiency and reduce costs with fractional GPUs.
Perfect for Jupyter Notebook Farms; Ideal for Inference.

gpu sharing

Run More Workloads on a Single GPU

Run multiple notebooks or host multiple inference servers on the same GPU to increase efficiency and reduce cost

Memory Isolation

Prevent Memory Collisions

Prevent collisions between workloads running on the same GPU with Run:ai Software Isolation. No code Change is required.

Compute Time Slicing

Ensure GPU Compute Allocations

Control how GPU Compute is shared between multiple workloads with advanced time slicing methods like Strict and Fairshare

Dynamic MIG

Remove Hassles Around MIG Configurations

Provision Multi-Instance GPU (MIG) slices on the fly without manual configurations, draining workloads, or rebooting your GPUs

Node Pools

Set Controls at the Node Pool Level

Set priorities, quotas, and policies for each Node Pool and ensure resource allocations and security controls are aligned with business goals even for the most heterogeneous clusters running T4s and H100s

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