Sold by: NVIDIA
Deployed on AWS
NVIDIA Run:ai is a Kubernetes-native platform for orchestrating AI workloads and optimizing GPU resources. Designed for ML and AI teams, it streamlines resource management, boosts GPU utilization, and accelerates development. Run:ai offers seamless integration with leading MLOps tools and cloud environments, enabling dynamic GPU scheduling, fractional GPU allocation, and automated workload orchestration.
Overview
*** This offering is only available via private offer - please contact your NVIDIA sales representative to initiate the process ***
NVIDIA Run:ai delivers an enterprise-grade AI workload orchestration platform that maximizes the efficiency and scalability of your AWS GPU infrastructure. Purpose-built for Kubernetes environments and optimized for AI/ML workloads, Run:ai enables AWS customers to achieve greater throughput, improved utilization, and faster model development - all while maintaining tight control over resources and costs.
Run:ai abstracts the complexity of managing GPU resources and accelerates time-to-insight for data science teams, while providing DevOps and IT stakeholders with robust tools for visibility, policy enforcement, and cost optimization. Run:ai ensures rapid deployment and integration with AWS-native services such as Amazon EKS and Amazon EC2 GPU instances, and AWS Identity and Access Management (IAM).
Key capabilities:
Flexible GPU Scaling for AI Workloads: Seamlessly scale GPU resources up or down across AWS environments to match the dynamic needs of training, tuning, and inference. Automated GPU Orchestration: Ensure optimal resource allocation and scheduling for multiple workloads using intelligent policies that minimize idle time. Team-Based Resource Governance: Use role-based access control and team-level quotas to ensure isolation, compliance, and shared infrastructure visibility across AI teams. Integration with AWS Services: Deploy alongside Amazon EKS and integrate with services like Amazon S3, CloudWatch, and IAM for a unified operational experience. MLOps Workflow Compatibility: Native support for JupyterHub, Kubeflow, MLflow, and other AWS-hosted tools to support end-to-end machine learning pipelines. With NVIDIA Run:ai, organizations can rapidly onboard AI teams, democratize access to GPU infrastructure, and accelerate innovation while keeping infrastructure flexible and cost-effective. The solution is ideal for enterprises looking to scale AI initiatives without the burden of managing complex infrastructure manually.
Note: This AMI includes the KAI Scheduler, an open-source GPU workload scheduler from NVIDIA. KAI provides a lightweight preview of a limited subset of the capabilities available in Run:ai's full enterprise platform.
Highlights
- Optimize GPU Usage at Scale: Run:ai eliminates idle GPUs by enabling fractional sharing and dynamic allocation, maximizing hardware efficiency across teams and workloads.
- Purpose-Built AI Scheduling: the Run:ai intelligent scheduler is designed specifically for AI workloads, using techniques like gang scheduling and preemption to efficiently manage complex training and inference jobs.
- Centralized Hybrid Control: Manage all GPU resources - on-prem, cloud, or hybrid - from a single control plane with full visibility, policy enforcement, and multi-tenant support.
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 24.04
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/unit |
|---|---|---|
GPU | Run:ai Annual Plan per GPU | $2,600.00 |
Custom pricing options
Request a private offer to receive a custom quote.
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Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
Additional details
Usage instructions
Prerequisites
- An AWS account with permissions to launch Amazon EC2 instances and manage IAM roles.
- Access to supported Amazon EC2 GPU instance types
- An existing Kubernetes cluster (Amazon EKS)
- Network connectivity that allows the Kubernetes cluster to communicate with the NVIDIA Run:ai control plane
- kubectl installed on a machine with access to the Kubernetes cluster
Deployment
- Accept the AWS Marketplace Private Offer for this product.
- Select a supported Amazon EC2 GPU instance type.
- Configure networking, IAM role, and security groups as required.
- Complete the instance launch.
Adding a Kubernetes Cluster to Run:ai Before installing the NVIDIA Run:ai cluster, verify that system and network requirements are met.
- Log in to the NVIDIA Run:ai platform.
- If this is the first cluster, the New Cluster form opens automatically. Otherwise, navigate to Resources and click + New Cluster.
- Enter a unique name for the cluster.
- (Optional) Select the NVIDIA Run:ai cluster version (latest by default).
- Enter the cluster URL (fully qualified domain name).
- Click Continue.
Installing the NVIDIA Run:ai Cluster
- Review the installation instructions presented in the platform.
- Run the provided installation commands on the target Kubernetes cluster.
Detailed installation instructions can be found here: https://run-ai-docs.nvidia.com/self-hosted/getting-started/installation/install-using-helm/helm-install
For details about KAI Scheduler open source: https://github.com/NVIDIA/KAI-Scheduler
Support
Vendor support
NVIDIA Run:ai customers receive Enterprise Business Standard Support for this solution:
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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