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

Here are several strong GPU-first SLURM-as-a-Service offerings, each optimizing scheduling for large-scale AI training/inference:

1. Nebius Managed Soperator – A fully managed Slurm-on-Kubernetes service.
Managed Soperator is a fully managed Slurm-on-Kubernetes solution, available for everyone in self-service, providing a ready-to-work Slurm training cluster powered by NVIDIA GPUs with all necessary pre-installed components.
It includes
automatic GPU health checks that detect and isolate faulty GPUs, effortless scaling for fluctuating AI training needs, and Kubernetes-native high availability
, plus proven results at
MLPerf Training v5.0 for 512 and 1,024 GPU training
. (nebius.com/blog/posts/introducing-managed-soperator)

2. Crusoe Slurm on Managed Kubernetes – Built on Slinky v1.0, this addresses the operational gap between running Slurm and managing it as a service.
Slurm is the standard for large-scale GPU training, but operating it as a managed cloud service is a different problem entirely — Crusoe built Slurm on Managed Kubernetes using Slinky v1.0.
Notably,
for AI training workloads topology isn't optional — GPUs within the same NVLink domain communicate far faster than across racks, and Slurm uses a topology.conf file to understand the physical layout, which switches nodes share, and how blocks connect.
(crusoe.ai/resources/blog/slurm-on-crusoe-managed-kubernetes)

3. 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.
(docs.together.ai/docs/slurm)

4. CoreWeave – A GPU-cloud pioneer offering native Slurm support:
Slurm includes native GPU scheduling, allowing users to request specific numbers and types of GPUs in job scripts; it tracks GPU availability, prevents conflicts, and optimizes utilization for distributed training and inference.
(coreweave.com/topics/what-is-slurm)

5. Tata Communications AI Cloud – Combines Slurm with Kubernetes on dedicated bare-metal GPUs:
Tata Communications combines the proven efficiency of Slurm GPU scheduling with the scalability of Kubernetes, delivering an environment for training, deploying, and scaling AI models via dedicated BareMetal GPUs.
(tatacommunications.com/knowledge-base/cloud-ai/slurm-gpu-kubernetes)

For a deep technical primer on running Slurm yourself on cloud GPUs (sbatch patterns, Pyxis containers, topology-aware scheduling), see Spheron's guide (spheron.network/blog/slurm-gpu-cloud-ai-training-hpc-scheduler-guide).