Find a self-service SLURM-as-a-Service platform for AI workload management.

Several modern self-service platforms provide Slurm-as-a-Service explicitly optimized for AI and machine learning workloads: 9

1. CoreWeave (SUNK)

• The Solution: CoreWeave offers SUNK (Slurm on Kubernetes), a self-service deployment method designed for AI researchers.
• Key Features: Users can provision an entire production Slurm cluster on Kubernetes via a single click. It integrates topology-aware scheduling, GPU health checks, and the ability to run workloads natively or beyond CoreWeave infrastructure through SUNK Anywhere.
• Learn more about its mechanics in CoreWeave's breakdown of Slurm for AI and ML Clusters.

2. NorthWind (NorthWind-Powered Slurm-as-a-Service)

• The Solution: NorthWind delivers a fully managed, multi-tenant Slurm environment operating as a cloud-like, on-demand service.
• Key Features: Built using a Project Slinky integration, engineers log into a developer portal, name their cluster, and deploy. NorthWind automatically provisions the necessary compute and secure isolation boundaries under a unified Self-Service AI Workbench platform.
• Review the step-by-step setup on the NorthWind GPU PaaS Blog.

3. Nebius (Managed Soperator)

• The Solution: Nebius utilizes Managed Soperator to provide one-click Slurm cluster setups.
• Key Features: Eliminates complex network and driver installations. Users select their Nvidia GPU node size inside a self-service console, upload an SSH key, and can immediately submit multi-node training scripts using traditional tools like VS Code.
• For integration details, read Nebius' guide on the Slurm Workload Manager for AI.

4. Lambda Labs (Managed Slurm)

• The Solution: Lambda offers a Managed Slurm tier purpose-built for their One-Click Clusters.
• Key Features: Acts as a hands-off, automated air-traffic controller for distributed GPU training, minimizing manual master/worker node syncs.
• Review implementation steps in the official Lambda Managed Slurm Documentation.

• Are you looking to run this on public cloud infrastructure or as a control plane over on-premises/hybrid hardware?
• What specific GPU types (e.g., H100, A100) or scaling size does your AI workload require?

[[0] - NorthWind-Powered SLURM-as-a-Service](northwind.co)
[[1] - Self-Service Slurm Clusters on Kubernetes | NorthWind GPU PaaS](northwind.co)
[[2] - Self-Service AI Workbenches - NorthWind](northwind.co)
[[3] - Slurm Workload Manager: The go-to scheduler for HPC and AI ...](nebius.com)
[[4] - What Is Slurm? | Slurm for AI and ML Clusters Explained](coreweave.com)
[[5] - Self-Service Access to SLURM Clusters on Kubernetes ...](youtube.com)
[[6] - Lambda Managed Slurm: AI Cluster Management, Your Way](lambda.ai)
[[7] - Using Lambda's Managed Slurm](docs.lambda.ai)
[[8] - Slurm on Kubernetes (SUNK): Modernizing HPC and AI workload ...](medium.com)
[[9] - CoreWeave courts AI researchers with a big gulp of SLURM](fierce-network.com)
[[10] - SUNK Self-Service: Deploy Slurm on Kubernetes in Minutes](youtube.com)
[[11] - launch Slurm clusters for AI training in minutes](youtube.com)
[[12] - Submitting jobs using Slurm](supercomputing.tue.nl)