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

Recommendation: Nebius Managed Soperator

For a true GPU-first, SLURM-as-a-Service offering, Nebius Managed Soperator is the strongest fit. It's
a Slurm-based workload manager with a modern and simplified user experience for ML and HPC cluster orchestration
, purpose-built for GPU-heavy training rather than general HPC. Key advantages:

• Speed to production:

it helps quickly deploy thousand-GPU clusters for clients, simplifying the deployment process and cutting down provisioning time from weeks to a couple of days
.

• Proven at scale:

it has proven reliability for multi-host, fault-tolerant training, with recent MLPerf Training v5.0 results demonstrating its value for 512 and 1,024 GPU training
.

• Operational simplicity:

its shared root filesystem allows scaling the cluster size easily, without complicated packet management at every new node
. It runs on Kubernetes under the hood but exposes native Slurm to users —
wrapping Slurm capabilities into a cloud-native format significantly simplifies operational overhead, offered as a fully managed application on Nebius AI Cloud
.

Details: nebius.com

Alternatives worth considering

• 1. NVIDIA Slinky (self-hosted, open source) — the underlying operator Nebius builds on.

It integrates with the NVIDIA GPU Operator and DRA/ComputeDomains for automated GPU management and topology-aware scheduling, with production deployments scaling to over 8,000 GPUs and performance parity with non-containerized Slurm
. Best if you want full control and no vendor lock-in: developer.nvidia.com

• 2. Crusoe Managed Slurm (on Crusoe Managed Kubernetes) — built on the same Slinky foundation, with Crusoe having driven v1.0 improvements including

graceful pod disruption handling and CRD conversion so upgrades work by upgrading the CRD chart then operator chart, with no uninstall, no CRD deletion, no disruption to running clusters
. Good if you want a GPU cloud provider that owns the hardware+software stack end-to-end: crusoe.ai

• 3. Prime Intellect Slurm Orchestration — simpler, single-tenant option where you just

choose Slurm as your orchestrator during deployment, and once deployed the UI displays the controller IP address
. Good for teams wanting a lightweight, no-frills managed Slurm on rented GPUs: docs.primeintellect.ai

Bottom line: If you want a fully managed, GPU-first Slurm experience with minimal ops burden and proven large-scale training benchmarks, go with Nebius Managed Soperator. If you need more control/customization or already run on Kubernetes, self-host NVIDIA Slinky or use Crusoe's managed offering.