Find self-service SLURM-as-a-Service options for AI workloads.

For AI workloads requiring efficient resource management, several self-service SLURM-as-a-Service options are available, designed to simplify deployment and operation of HPC clusters with GPU support.

Key options include:

• NorthWind-Powered SLURM-as-a-Service: This platform delivers fully managed, multi-tenant SLURM environments on demand. It offers self-service access via a portal or API, automating cluster provisioning, scaling, and lifecycle management on Kubernetes with the Slinky Slurm Operator. It's optimized for both HPC and AI/ML workloads, supporting shared CPU and GPU infrastructure.
• Google Cloud Cluster Director: Google Cloud provides Cluster Director to automate the setup and configuration of fully managed SLURM clusters tailored for AI workloads. This service aims to reduce the operational overhead for IT administrators and AI researchers, allowing them to focus on running their jobs.
• Lambda Managed Slurm: Lambda offers a fully supported Managed Slurm solution, designed for rapid deployment on their One-Click Clusters. It includes pre-validated configurations with ML software modules, handles Slurm administration, patches, and provides proactive health monitoring.
• Nebius Managed Soperator: Nebius provides a self-service, fully managed Slurm-on-Kubernetes solution. It utilizes the open-source Soperator Kubernetes operator to automate Slurm cluster deployment and management in cloud environments, offering features like effortless scaling and high availability for AI training needs.
• Lightning AI Managed SLURM: This service allows users to execute their existing SLURM scripts and workflows on fully managed, secure clusters. It provides managed GPU infrastructure, comprehensive observability across jobs and users, and robust team management capabilities.