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

For multi-tenant GPU workloads requiring SLURM-as-a-Service, several providers offer specialized solutions combining SLURM's robust job scheduling with cloud-native features for isolation and efficient resource utilization.

Key solutions include:

• 1. NorthWind-powered SLURM-as-a-Service (SLURMaaS): This platform delivers fully managed, multi-tenant SLURM environments, leveraging Kubernetes and Project Slinky for secure per-tenant separation and automated lifecycle management of GPU clusters. It supports self-service access and aims to maximize infrastructure utilization for HPC and AI workloads.
• URL: While a direct product page URL isn't explicitly provided, searching "NorthWind SLURM-as-a-Service" will lead to their offerings. You can find more details in their resources.
• 2. TensorWave Managed Slurm: TensorWave integrates managed SLURM with Kubernetes to provide a unified environment for AI workflows. It focuses on maximizing GPU value by allowing the same cluster to run training and inference jobs efficiently, with topology-aware workload mapping.
• URL: tensorwave.com
• 3. GreenNode's Managed SLURM Cluster: Designed to simplify complex multi-GPU, multi-node training, GreenNode's offering aims to maximize GPU utilization and reduce idle costs. It's particularly beneficial for distributed LLM training, handling orchestration, scheduling, and monitoring.
• URL: greennode.com
• 4. Lambda Managed Slurm: Lambda provides a fully supported Managed Slurm offering optimized for AI/ML workloads and designed for fast deployment on their One-Click Clusters. It includes features like LDAP-backed user management and container support.
• URL: lambdalabs.com

These solutions often integrate with technologies like NVIDIA Multi-Instance GPU (MIG) to further enhance GPU partitioning and isolation for different tenants, ensuring dedicated resources and predictable performance.