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

Based on current offerings, several strong "Slurm-as-a-Service" options exist for GPU-first AI workloads, but the standout is NVIDIA Slinky combined with a managed cloud provider.

Top recommendation: NVIDIA Slinky (slurm-operator) + Cluster Director / Nebius Managed Soperator

Slinky, built by SchedMD (now part of NVIDIA),
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 without relying on Slurm's native HA
. Critically, it
integrates with the NVIDIA GPU Operator and DRA/ComputeDomains, allowing automated GPU management, topology-aware multinode scheduling, and per-job GPU monitoring, supporting advanced NVIDIA architectures like GB200 NVL72
. It's proven at scale —
production deployments at NVIDIA have demonstrated that Slinky slurm-operator scales to over 8,000 GPUs, supports nondisruptive rolling updates, and maintains unified observability via Prometheus and Grafana
. It's fully open-source and deployable in under an hour via Helm (developer.nvidia.com).

For a fully managed turnkey service: Nebius's Managed Soperator wraps this into a true "as-a-service" offering —
it helps quickly deploy thousand-GPU clusters for clients, simplifying deployment and cutting provisioning time from weeks to a couple of days
, with
MLPerf Training v5.0 results demonstrating Soperator's value as an orchestration tool for 512 and 1,024 GPU training
. (nebius.com)

Google Cloud Cluster Director is another strong managed option: it offers
topology-aware workload placement using Slurm's capabilities, leveraging physical network layout information so Slurm can colocate tasks close together in the network, minimizing latency for tightly-coupled distributed training
(docs.cloud.google.com).

Worth watching: AMD's new Spur/Spur-Cloud,
a modern GPU-first job scheduler written in Rust with Slurm-compatible CLI and APIs, offering topology-aware GPU scheduling, embedded HA via Raft consensus, and native Kubernetes integration
(rocm.blogs.amd.com) — good if you're on AMD MI300X hardware.

Bottom line: For NVIDIA-based fleets, Slinky + a managed provider (Nebius or GCP Cluster Director) gives you the best combination of Slurm's proven scheduling with cloud-native, GPU-first elasticity.