Run:ai

Run:ai is intended to optimize and orchestrate GPU compute resources for AI and deep learning workloads. The platform is designed to accelerate AI development by automatically managing AI workloads and resources to maximize GPU usage.

Run:ai includes a range of features to support AI teams:

• Run:ai Dev: Speeds AI development with end-to-end lifecycle support from idea to deployment, including workspaces, training, fine-tuning and private LLMs.
• Run:ai Control Plane: Automatically manages AI workloads and resources to maximize GPU usage with features like AI workload scheduling, node pooling and container orchestration.
• Run:ai Cluster Engine: Gives you control and visibility into AI infrastructure, workloads and users so you can manage resources and utilization effectively.

The platform supports a range of tools and frameworks, including Jupyter Notebook, Pycharm, VScode, W&B, Comet.ml, MLflow and Tensorboard. Customers can deploy on their own infrastructure, whether in the cloud, on-premise or air-gapped environments, and use any ML tool and framework with any Kubernetes setup.

Run:ai is designed for efficiency, security and scalability. Among its benefits:

• Maximum Efficiency: Supports up to 10 times more workloads on the same infrastructure with dynamic scheduling and resource pooling.
• Secured & Controlled: Offers fair-share scheduling, quota management, priorities and policies for secure and controlled resource allocation.
• Full Visibility: Gives you insights into infrastructure and workload utilization across clouds and on-premise environments.

Run:ai is for data scientists, MLOps engineers and DevOps teams who want to simplify their AI development and infrastructure management. The platform helps companies accelerate their AI projects, optimize resource usage and lower costs.

You can learn more about Run:ai by visiting the company's website to check out its guides, case studies and documentation, or schedule a demo to see the platform firsthand.

Published on August 5, 2024