Description
NVIDIA Run:ai is an enterprise platform for AI workload and GPU orchestration, addressing infrastructure challenges that come up once an organization is running AI training and inference across shared GPU clusters rather than a single machine.
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The platform's core job is dynamic resource allocation: it pools GPU capacity across environments and matches resources to workload demand in real time, so idle GPU time in one project can be used by another. A policy engine sits on top of this pooling, letting an organization run workloads across departments, projects and teams with centralized, policy-driven governance that determines who gets prioritized, reliable access to GPU resources. Run:ai supports public cloud, private cloud, hybrid environments and on-premises data centers, and its open, API-first architecture is built to integrate with major AI frameworks, machine learning tools and third-party systems rather than lock a team into one stack.
NVIDIA cites vendor benchmarks of up to 10x GPU availability, 20x more workloads running, and 5x GPU utilization from customers using dynamic scheduling and orchestration, though exact gains will vary by environment and workload mix. Beyond core scheduling, Run:ai's current release (v2.25 as described on the product page) is described as advancing a unified platform for building and operating AI systems at production scale, including distributed workload orchestration and inference scaling with more visibility and control.
Several components are open source: the KAI Scheduler is an open-source Kubernetes scheduler for AI workloads, built on Run:ai and configured through plain YAML files, aimed at developers and small teams. Grove bridges AI inference frameworks and Kubernetes scheduling for topology-aware, gang-scheduled deployments, and can run standalone or alongside other inference frameworks. The Run:ai Model Streamer is a Python SDK with a C++ backend that reads model tensors concurrently from storage and streams them directly into GPU memory, cutting model loading times in inference workloads from minutes to seconds.
NVIDIA Run:ai is now bundled into NVIDIA AI Enterprise and also powers scheduling inside NVIDIA Mission Control, NVIDIA's broader AI operations platform. It is built for enterprise ML platform and infrastructure teams operating shared GPU clusters at scale, rather than for an individual developer training a model on their own machine; adopting it assumes an existing Kubernetes-based or NVIDIA-supported GPU infrastructure to orchestrate. Pricing is not published and is handled through direct or partner sales.
- CategoryMLOps & Model Deployment
- Pricing ModelContact sales
- DeploymentCloud, On-premises
- PlatformsWeb
NVIDIA Run:ai's Core Features
AI-native workload orchestration centralizing execution across distributed environments
Dynamic GPU allocation matching resources to workload demand in real time
Policy-driven governance for fair, prioritized GPU access across departments and teams
Open, API-first architecture integrating with major AI frameworks and ML tools
Support for public cloud, private cloud, hybrid and on-premises deployment
KAI Scheduler, an open-source Kubernetes scheduler for AI workloads
Grove, topology-aware scheduling for AI inference on Kubernetes
Run:ai Model Streamer, cutting model load times from minutes to seconds
Bundled into NVIDIA AI Enterprise and NVIDIA Mission Control
How to use NVIDIA Run:ai?
Deploy Run:ai on your Kubernetes-based GPU infrastructure, in the cloud, on-premises or hybrid
Pool GPU resources by connecting clusters so GPUs are shared across teams and projects
Set governance policies for fair, prioritized access to shared GPU capacity
Submit training and inference workloads for Run:ai to schedule and allocate dynamically
Monitor utilization centrally and scale workloads across environments as demand changes
Integrate existing ML frameworks and tools through the platform's open, API-first architecture
NVIDIA Run:ai's Use Cases
- GPU orchestration for AI training
- Scaling AI inference
- Multi-tenant GPU governance
- Reducing model cold starts
