The best Slurm Workload Manager alternatives

Backend.AI suits teams or institutions building and operating AI workloads, from single-GPU setups to large clusters.

It suits teams developing, training, and deploying AI/ML models that need GPU infrastructure management and experiment tracking.

dstack suits teams managing AI workloads across cloud and on-premises infrastructure, including teams that need to provision resources and schedule…

GPUStack suits teams deploying AI models across on-premise, cloud, or hybrid infrastructure.

It suits academic, government, and commercial organizations managing distributed computational workloads across local, cloud, or grid resources.

OpenPBS suits organizations running HPC clusters, clouds, or supercomputers that need job scheduling and workload management.

TORQUE is for administrators managing compute clusters who need a self-hosted resource and queue manager.

HAMi suits teams running AI, machine-learning, LLM, or HPC workloads on Kubernetes that need to share accelerators and control resource requests.

SkyPilot suits AI teams coordinating workloads across cloud, on-premises, Kubernetes, Slurm, and VM infrastructure.

YuniKorn suits teams scheduling batch, data, or machine learning workloads on Kubernetes.

Koordinator suits teams running mixed Kubernetes workloads that need scheduling priorities, QoS controls, resource sharing, or isolation.

Kueue suits teams running batch or other supported workloads on Kubernetes that need quota management and scheduling controls.

KAI Scheduler suits teams running GPU-intensive AI workloads on Kubernetes.

It suits HPC administrators and organizations managing Linux clusters that need policy-based scheduling, resource controls, multi-cluster monitoring,…

This suits administrators managing Linux-based servers, nodes, or systems who need deployment and lifecycle tools.