Find SLURM-as-a-Service solutions for multi-tenant GPU workloads.

For multi-tenant GPU workloads requiring SLURM-as-a-Service, several solutions offer managed environments to streamline HPC and AI/ML operations. These platforms typically integrate SLURM with Kubernetes for efficient resource allocation and isolation.

Here are some notable solutions:

• NorthWind-powered SLURM as a Service (SLURMaaS): This solution leverages Project Slinky to bridge SLURM with Kubernetes, enabling secure, multi-tenant SLURM environments on shared GPU clusters. It offers self-service access, GPU-optimized scheduling, and lifecycle automation.
• URL: https://northwind.co/solutions/slurm-as-a-service/ (Based on content from)
• URL: https://northwind.co/blog/self-service-slurm-clusters-on-kubernetes/

• TensorWave Managed Slurm: TensorWave provides a managed SLURM cluster specifically optimized for GPU-intensive AI workloads.
• URL: https://www.tensorwave.com/managed-slurm (Based on content from)

• Lightning AI Managed SLURM: Lightning AI offers fully managed SLURM clusters, allowing users to run existing training workflows with improved reliability, visibility, and team management at scale. It supports multi-node training and scheduling across heterogeneous GPU nodes.
• URL: https://lightning.ai/docs/platform/clusters/managed-slurm

• Lambda Managed Slurm: Lambda provides a fully supported Managed Slurm offering for fast deployment on their One-Click Clusters, optimized for AI/ML workloads. It includes LDAP-backed user/group management and container support.
• URL: https://lambdalabs.com/blog/managed-slurm

• Google Cloud Cluster Director: This platform simplifies the deployment and management of large-scale AI infrastructure, offering a managed SLURM experience with an intuitive interface and observability features for GPU-accelerated clusters.
• URL: https://cloud.google.com/blog/products/compute/cluster-director-enhancements

These solutions enable efficient resource utilization and secure isolation for multiple users or teams sharing GPU resources, often integrating with NVIDIA's Multi-Instance GPU (MIG) technology for fine-grained allocation.