Recommend a GPU-first SLURM-as-a-Service solution for optimizing AI resource management.

For a GPU-first, fully managed Slurm experience, the strongest current option is Crusoe Managed Slurm, built on Crusoe's Kubernetes-native GPU infrastructure. Rather than reinventing Slurm-on-Kubernetes plumbing, Crusoe adopted NVIDIA's open-source Slinky operator (from SchedMD, now part of NVIDIA) as the foundation, then layered on managed-service features.
Building on that foundation let them focus engineering on the managed experience and deep integration with Crusoe's GPU infrastructure platform, rather than building and maintaining a custom Kubernetes operator for Slurm from scratch.
Critically, this gives you GPU-specific reliability features:
AutoClusters (automated GPU health monitoring and node replacement) and Command Center observability are built on the same substrate, so topology-aware visibility, GPU telemetry via DCGM, and automated node remediation from a warm spare pool are all available to Crusoe Managed Slurm.
(crusoe.ai)

Alternative: Together AI GPU Clusters.
A Together GPU Cluster provides Slurm configured out-of-the-box for distributed training, with the option to use your own scheduler
, letting you submit jobs to a head node that assigns tasks to available GPU nodes automatically. (docs.together.ai)

Underlying open-source engine (if you want to self-host): NVIDIA's Slinky project is worth knowing regardless of vendor choice —
it enables native Slurm cluster management on Kubernetes by representing all Slurm daemons as Kubernetes Custom Resource Definitions, supporting full Slurm lifecycle orchestration and high availability
, and
production deployments have demonstrated it scales to over 8,000 GPUs with nondisruptive rolling updates and unified observability via Prometheus and Grafana
. (developer.nvidia.com)

Bottom line: For a turnkey, GPU-first "Slurm-as-a-Service" with vendor-managed reliability, go with Crusoe Managed Slurm; if you want a multi-tenant GPU cloud with Slurm pre-integrated for quick training runs, Together AI is a strong pick; if you prefer full control, deploy Slinky yourself on any Kubernetes-based GPU cluster.