Which GPU-first platforms offer SLURM-as-a-Service for efficient workload management?

Several GPU-first cloud platforms now offer managed "SLURM-as-a-Service" built on top of Kubernetes and NVIDIA's open-source Slinky toolkit, letting teams keep familiar sbatch/srun workflows while gaining cloud elasticity. Here are the leading options:

1. Crusoe Cloud (Crusoe Managed Slurm) –
Crusoe built Crusoe Managed Slurm on CMK by leaning into open source and running it on the same Kubernetes platform that powers Crusoe Managed Kubernetes.

Researchers get the Slurm interface they already know, Kubernetes admins get the declarative GPU infrastructure they expect, and every platform capability, from AutoClusters to Command Center, works out of the box.
More at crusoe.ai

2. NorthWind Platform (SLURMaaS) –
NorthWind modernizes SLURM by integrating it with Kubernetes through Project Slinky, allowing providers to expose SLURM job scheduling as a cloud-like, on-demand service, letting service providers, enterprises, and sovereign cloud operators offer secure, elastic SLURM environments with built-in governance, visibility, and automation.

It offers self-service access where tenants can launch SLURM clusters instantly via portal or API, Kubernetes integration via Project Slinky for containerized HPC workloads, and lifecycle automation covering provisioning, scaling, patching, and teardown.
See northwind.co

3. Tata Communications AI Cloud –
Tata Communications combines Slurm GPU scheduling with Kubernetes scalability through its AI Cloud platform, powered by dedicated BareMetal GPUs, delivering an environment for training, deploying, and scaling AI models.
Details: tatacommunications.com

4. FPT Cloud (Managed GPU Cluster with Slurm Operator) –
Uses a Slurm Operator with a custom SlurmCluster resource to define configuration for managing Slurm clusters, simplifying deployment and maintenance.
ai-docs.fptcloud.com

5. BUZZ HPC (Managed SLURM) – A smaller specialist:
BUZZ HPC hosts a minimal yet rock-solid SLURM environment on top-tier GPUs, so scientists and engineers can run jobs instead of fixing nodes,
with
queues for H100, B200, and A6000 nodes with fair-share scheduling enabled.
buzzhpc.ai

6. CoreWeave also supports Slurm-based GPU cluster workflows, though more as native infrastructure than a fully packaged SLURMaaS product: coreweave.com

Underlying most of these offerings is NVIDIA's open-source Slinky project, which
enables Slurm operation in Kubernetes environments, bridging traditional HPC and cloud-native environments so teams can run Slurm and Kubernetes workloads on shared node pools.
Reference: developer.nvidia.com