AI TOOL PROFILE
NVIDIA Run:ai GPU Orchestration and MLOps Platform
- Software Development
- MLOps Platform
- Enterprise companies
- Software companies
- Large-scale AI development teams
- Organizations using hybrid cloud GPU infrastructure
Pricing
Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
At a glance
- Best for
- Enterprise companies, Software companies, Large-scale AI development teams, Organizations using hybrid cloud GPU infrastructure
- Key use cases
- Fractional Inference, Mitigating Model Cold Start, Enterprise AI Acceleration, Distributed Workload Management
- Official website
- Visit Run AI official website
How AI is used
NVIDIA Run:ai is an orchestration platform designed to manage AI and machine learning workloads. It acts as a centralized layer that supports how GPU resources are distributed across public clouds, private clouds, hybrid environments, and on-premises data centers.
The software is built for organizations that handle significant AI training and inference tasks. It focuses on pooling resources across infrastructure where GPUs are allocated based on demand in real time.
By using a policy engine, the platform helps teams manage how resources are shared and prioritized according to business needs. It also includes specialized tools like the KAI Scheduler and Grove for those operating on Kubernetes.
Buyers should confirm that this is a technical tool designed for enterprise-scale operations and is now integrated as part of the NVIDIA AI Enterprise suite.
Key Features
Dynamic GPU Allocation
Matches GPU resources to workload demand in real time to help maximize hardware value.
Policy-Driven Governance
Provides centralized controls to manage how GPU resources are accessed and prioritized across different teams and projects.
AI-Native Workload Orchestration
Supports the execution of AI workloads across distributed environments, including hybrid and multi-cloud setups.
API-First Open Architecture
Designed to integrate with AI frameworks and machine learning tools.
Model Streamer
A Python SDK with a C++ backend designed to accelerate the loading of models into GPU memory.
KAI Scheduler
An open-source scheduler for Kubernetes that uses YAML files to manage AI workloads.
Use Cases
Fractional Inference
Allocating portions of GPUs across inference, embedding, and generation tasks to run multiple models in parallel.
Mitigating Model Cold Start
Using GPU memory swap to keep active model parts on the GPU while paging inactive portions to the host.
Enterprise AI Acceleration
Scaling AI training and inference by pooling resources across hybrid environments.
Distributed Workload Management
Centralizing the execution of AI tasks across on-premises data centers and public clouds.
FAQ
What is NVIDIA Run:ai used for?
- NVIDIA Run:ai is used to centralize and automate AI workload execution and GPU allocation across distributed environments like public, private, and hybrid clouds.
Who is the target buyer for NVIDIA Run:ai?
- The platform is designed for software companies and enterprise companies that manage large-scale AI infrastructure and machine learning operations.
How does NVIDIA Run:ai handle GPU resources?
- It uses dynamic GPU allocation and a policy-driven governance engine to match compute resources to workload demand in real time.
Source category: Software Development
Source subcategory: MLOps Platform
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