Run:AI

Inactive

Run:ai is an AI cluster management platform that optimizes GPU resource utilization through dynamic scheduling, GPU fractioning, and workload management. It enables organizations to increase AI workload efficiency by up to 10 times, accelerating development and reducing operational costs.

Traction 6/10

Tel Aviv, Israel · HQ

Founded 2018

Artificial Intelligence
Developer Tools
Enterprise Software
Software Only

Updated yesterday

Funding

Funding not disclosed

Funding rounds are not available yet.

Founders

Founder details are not available yet.

Product

Problem

AI development is often hindered by inefficient GPU resource allocation, leading to underutilized infrastructure and increased operational costs. Traditional methods lack the dynamic scheduling and workload management capabilities needed to maximize GPU utilization across diverse AI workloads.

Solution

Run:ai provides an AI cluster management platform that optimizes GPU resource utilization through dynamic scheduling, GPU fractioning, and workload management. The platform creates a virtual pool of compute resources and automates allocation, enabling organizations to run up to 10 times more AI workloads on the same infrastructure. By offering features like fair-share scheduling, quota management, and policy enforcement, Run:ai ensures that resources are allocated efficiently and according to business priorities. The platform also provides comprehensive dashboards and reporting tools for gaining insights into infrastructure and workload utilization across cloud and on-premise environments.

Target Audience

The primary target audience includes data scientists, machine learning engineers, and IT professionals in organizations that are developing and deploying AI models at scale.

Features

Dynamic scheduling to optimize resource allocation for AI workloads
GPU fractioning to increase cost efficiency for notebook farms and inference environments
Node pooling to control heterogeneous AI clusters with quotas, priorities, and policies
AI workload scheduler tailored for the entire AI lifecycle
Multi-cluster management for centralized control across diverse environments
Comprehensive dashboards and reporting for insights into infrastructure and workload utilization
Support for various ML tools and frameworks, including open-source options
Integration with Kubernetes for container orchestration