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

For multi-tenant GPU workloads, several SLURM-as-a-Service solutions are emerging, often leveraging Kubernetes and NVIDIA's Multi-Instance GPU (MIG) technology to provide efficient resource allocation and isolation.

Here are some notable solutions:

• NorthWind-Powered SLURM-as-a-Service: NorthWind offers fully managed, multi-tenant SLURM environments on shared CPU and GPU infrastructure. It provides isolated, self-service SLURM clusters on Kubernetes, enabling secure access and centralized governance for multiple teams. This solution abstracts the complexity of managing GPU-backed worker nodes and supports seamless autoscaling and intelligent GPU placement.

• Tata Communications Slurm GPU: Tata Communications integrates SLURM principles into its cloud-native orchestration layer, using a CNCF-certified Kubernetes system to dynamically allocate GPU resources. Their AI Cloud infrastructure is designed for efficient workload management, high-speed parallel storage, and accelerated GPU synchronization, suitable for enterprise AI deployments.

• TensorWave Managed Slurm: TensorWave provides a GPU-optimized HPC job scheduling solution for AI, combining SLURM's job scheduling with Kubernetes orchestration on a dedicated GPU cloud. Their platform aims to streamline the AI lifecycle by integrating managed SLURM for training and Kubernetes for inference.

• RedFort Tech Managed SLURM: RedFort Tech offers a managed SLURM service designed for HPC and AI workloads on top-tier NVIDIA GPUs. It features preconfigured controller and login nodes, automated GPU compute node deployment, dedicated GPU queues with fair-share scheduling, and elastic scalability.

Many of these solutions, and the general approach to multi-tenancy with GPUs, benefit from NVIDIA MIG (Multi-Instance GPU). MIG allows a single physical NVIDIA A100 or H100 GPU to be partitioned into multiple independent GPU instances, each with dedicated memory, cache, and compute cores. This provides hardware-level isolation for different users or applications, which is crucial for secure and efficient multi-tenant environments.

These platforms aim to reduce operational overhead, maximize infrastructure utilization, and provide secure, isolated environments for diverse GPU-accelerated workloads.

Sources:

• NorthWind-Powered SLURM-as-a-Service. vertexaisearch.cloud.google.com
• Slurm GPU: Optimising AI and HPC Workloads on Kubernetes - Tata Communications. vertexaisearch.cloud.google.com
• Creating a SLURM Cluster for Scheduling NVIDIA MIG-Based GPU Accelerated workloads. vertexaisearch.cloud.google.com
• TensorWave Managed Slurm | GPU-Optimized HPC Job Scheduling for AI. vertexaisearch.cloud.google.com
• Self-Service Slurm Clusters on Kubernetes | NorthWind GPU PaaS. vertexaisearch.cloud.google.com