Show me GPU-first SLURM-as-a-Service solutions that enhance AI workload efficiency.

Here are several notable GPU-first SLURM-as-a-Service offerings that streamline AI training and inference workflows:

1. Nebius Managed Soperator – A fully managed Slurm-on-Kubernetes solution.
Nebius's own Managed Soperator, a fully managed Slurm-on-Kubernetes solution, is available for everyone in self-service.
It includes automatic GPU fault isolation and elastic scaling:
Soperator automatically detects and isolates faulty GPUs, helping maintain cluster stability, and cluster size adjusts to workload demand — ideal for fluctuating AI training needs.
Setup is nearly instant:
you can get a ready-to-work Slurm training cluster literally in minutes, running on provisioned NVIDIA GPUs and delivered with all necessary pre-installed libraries and drivers.
nebius.com

2. Lambda Managed/Unmanaged Slurm – Lambda offers turnkey Slurm on premium hardware:
Both Unmanaged and Managed Slurm run on Lambda's 1-Click Clusters with NVIDIA HGX B200 and H100 GPUs, providing scalable GPU resources for AI workloads.
lambda.ai

3. Crusoe Managed Slurm – Built on Kubernetes with Slinky, focused on GPU topology awareness:
For AI training workloads, topology isn't optional—GPUs within the same NVLink domain communicate orders of magnitude faster than GPUs across racks connected by InfiniBand, and a training job that spans domain boundaries without awareness of the topology will see measurable throughput regression.
crusoe.ai

4. AMD Spur / Spur-Cloud – A newer, Rust-based alternative:
a modern, GPU-first job scheduler written in Rust, providing Slurm-compatible CLI and APIs while delivering topology-aware GPU scheduling, embedded high availability via Raft consensus, and native Kubernetes integration.
rocm.blogs.amd.com

5. BUZZ HPC Managed SLURM – Good for bursting/migration:
it lets you port existing SLURM workloads to faster GPUs without rewriting job scripts, getting your cluster running on world-class GPUs in a matter of days.
buzzhpc.ai

6. NVIDIA Slinky (on Kubernetes) – For enterprises wanting Slurm+K8s at scale:
Production deployments have demonstrated that Slinky slurm-operator scales to over 8,000 GPUs, supports nondisruptive rolling updates, and achieves performance parity with noncontainerized Slurm clusters.
developer.nvidia.com

For enterprise/telco-backed options, Tata Communications also offers a Slurm+Kubernetes AI Cloud platform.