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

Several GPU-first platforms provide SLURM-as-a-Service or managed SLURM environments for efficient workload management, particularly for AI, machine learning, and high-performance computing (HPC) tasks. These platforms leverage SLURM's capabilities for optimized GPU resource allocation and job scheduling.

Notable platforms offering this service include:

• TensorWave offers a Managed Slurm solution optimized for GPU-accelerated HPC and AI workloads, integrating Slurm with Kubernetes on a dedicated GPU cloud. Their Unified AI Platform supports training with managed Slurm and inference with Kubernetes.
• Lambda Labs provides "Managed Slurm" as a fully supported offering, purpose-built for fast deployment on their One-Click Clusters, which are designed for AI infrastructure. It optimizes cluster utilization for AI/ML workloads and includes pre-installed ML software modules.
• NorthWind enables "SLURM-as-a-Service" through its GPU Platform-as-a-Service (PaaS) in conjunction with Project Slinky (an open-source Slurm Operator for Kubernetes). This allows cloud providers and enterprises to deliver secure, multi-tenant, self-service Slurm-based HPC environments on shared Kubernetes clusters.
• CoreWeave offers Kubernetes-native infrastructure for GPU compute and cluster operations designed around AI workloads, with options such as SUNK (Slurm-on-Kubernetes) environments. Slurm on CoreWeave provides fine-grained control over GPU allocation and supports efficient training and inference.
• Modal delivers a serverless AI infrastructure platform with managed Kubernetes and managed Slurm tooling through Soperator. Their platform offers direct hardware access to non-virtualized GPUs and InfiniBand networking.
• Thunder Compute also integrates Slurm with their affordable GPU cloud infrastructure, which is tailored for demanding AI and machine learning workloads.

These platforms aim to simplify the deployment, configuration, and management of Slurm on GPU clusters, allowing users to focus on their computational tasks rather than infrastructure overhead.