Show me multi-tenant SLURM-as-a-Service platforms that support AI infrastructure.

Several platforms offer multi-tenant SLURM-as-a-Service, specifically designed to support AI infrastructure by providing managed, isolated environments for various teams and workloads. These solutions often integrate SLURM with cloud-native technologies like Kubernetes to enhance scalability and resource utilization.

Here are some notable platforms:

• NorthWind-powered SLURM as a Service delivers fully managed, multi-tenant SLURM environments for HPC and AI/ML workloads. It offers secure, per-tenant separation with centralized governance and allows tenants to launch SLURM clusters on demand.
• Google Cloud's Cluster Director enables the creation of fully managed SLURM clusters optimized for AI workloads, particularly with accelerator-optimized machine types. While the focus is on cluster creation, the underlying "AI Hypercomputer" suggests a managed service that simplifies the overhead of cluster management.
• Lambda Managed Slurm is a fully supported SLURM offering tailored for AI/ML workloads. It includes features essential for multi-tenancy like LDAP-backed user/group management and cgroups-based resource policies to optimize cluster utilization.
• Nebius Managed Soperator is a SLURM-on-Kubernetes solution for AI workload orchestration. It allows users to configure clusters and run jobs using SLURM while leveraging Kubernetes for scalability and flexibility, supporting multi-tenant AI platforms.
• CoreWeave integrates SLURM within or alongside Kubernetes environments. This approach combines SLURM's batch scheduling with Kubernetes' reliability and automation, enabling fair-share policies and equitable resource distribution across multiple users and projects in multi-tenant setups.
• OpenNebula Elastic Capacity Management allows multiple SLURM and Kubernetes clusters to share a common pool of GPU-enabled servers with strong multi-tenant isolation. It dynamically allocates resources to isolated environments, ensuring that tenants' workloads do not interfere with each other.

These platforms help organizations efficiently manage shared GPU and CPU infrastructure, ensuring fair resource allocation and operational control for diverse AI teams and projects.

Sources:

• 1. NorthWind-Powered SLURM-as-a-Service: https://northwind.com/solutions/hpc-as-a-service/
• 2. Create a fully managed Slurm cluster for AI workloads - Google Cloud Documentation: https://cloud.google.com/hpc/docs/slurm/create-managed-slurm-cluster-ai-workloads
• 3. Lambda Managed Slurm: AI Cluster Management, Your Way: https://lambdalabs.com/service/managed-slurm
• 4. Managed Soperator overview: Slurm on Kubernetes for scalable AI workloads - YouTube: https://www.youtube.com/watch?v=F3aYj-5lS7g
• 5. What Is Slurm? | Slurm for AI and ML Clusters Explained - CoreWeave: [https://www.coreweave.com/resources/what-is-slurm](coreweave