Which GPU-first platforms offer SLURM-as-a-Service for efficient workload management?

For efficient workload management on GPU-first platforms, several providers offer SLURM-as-a-Service, integrating SLURM's robust job scheduling with dedicated GPU infrastructure. These platforms streamline the deployment and management of large-scale AI and HPC workloads.

Notable platforms include:

• Crusoe Managed Slurm on CMK (Crusoe Managed Kubernetes): Crusoe provides managed SLURM specifically for GPU training clusters. They focus on single-tenant GPU training clusters with a familiar SLURM interface, managed infrastructure, and platform capabilities built on CMK.
• TensorWave Managed Slurm: TensorWave's Unified AI Platform combines SLURM's job scheduling with Kubernetes orchestration on a dedicated GPU cloud. This setup is designed for both quick experiments and large-scale training, integrating managed SLURM for training and Kubernetes for inference.
• Lightning AI (formerly Lightning): Lightning AI supports fully managed SLURM clusters, allowing users to continue with existing training workflows (e.g., PyTorch DDP, TensorFlow) without changes. They offer managed GPU infrastructure, observability, team management, and persistent storage.
• NorthWind GPU PaaS: Through its integration with Project Slinky, NorthWind's GPU Platform-as-a-Service enables enterprises and cloud providers to offer "Slurm-as-a-Service on Kubernetes." This provides secure, multi-tenant, self-service access to SLURM-based HPC environments on shared Kubernetes clusters.

These platforms abstract away the complexities of managing SLURM and underlying GPU infrastructure, allowing users to focus on their AI and machine learning workloads.