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

For multi-tenant GPU workloads requiring SLURM-as-a-Service, several providers offer robust solutions. These platforms typically handle the underlying infrastructure, allowing users to focus on their research and development without managing complex HPC environments.

Key solutions include:

• Lightning AI Managed SLURM: This service supports existing training workflows and is designed for multi-node training, scheduling across diverse GPU nodes, and managing priority queues with fair-share policies. It also includes team management and Role-Based Access Control (RBAC) for multi-tenant environments.
• Crusoe Cloud Managed Slurm: Built on Crusoe Managed Kubernetes, this solution provides managed HPC cluster orchestration specifically for GPU-optimized infrastructure. It features multi-user access, shared storage, and topology-aware scheduling, making it suitable for multi-tenant GPU training.
• TensorWave Managed Slurm: TensorWave offers a GPU-optimized HPC job scheduling service for AI, providing end-to-end operation of your SLURM cluster.
• Lambda Managed Slurm: Lambda provides both managed and unmanaged Slurm options on GPU clusters, including those powered by NVIDIA HGX B200/H100. It focuses on scalability and efficient job management for AI workflows.

These platforms abstract away the complexities of setting up and maintaining SLURM, offering features like robust scheduling, resource isolation, and user management essential for multi-tenant GPU environments.