Managed Slurm
Unified AI: Managed Slurm & Kubernetes
Managed Slurm for AI teams who want to run large-scale training and research on dedicated GPU cloud clusters; without burning time on cluster setup, tuning, or maintenance.
Our Solution
Tensorwave’s Stable Slurm Cluster, Operated End-to-End.
Managing disjointed systems for training and cloud-native deployment creates complexity, drains your budget through low GPU utilization, and slows your time-to-market.
TensorWave integrates managed Slurm and Kubernetes into a unified environment. We’ve eliminated the silos, so your team can train, deploy, and scale AI models seamlessly, all in one place.
Maximize GPU Value
Use the same cluster to run training jobs during off-peak hours and inference during peak demand, while topology-aware workload mapping keeps traffic local to the right GPUs; boosting utilization and driving costs down.
Streamline Your Workflow
Move from research to production faster with a unified platform, cutting delays and simplifying management for your team.
Unified Slurm + Kubernetes for AI Workloads
Combine Slurm's job scheduling with Kubernetes orchestration on a dedicated GPU cloud, making it easy to run everything from quick experiments to large-scale training.
TensorWave's Unified AI Platform integrates managed Slurm for training and Kubernetes for inference, streamlining your AI lifecycle on one efficient cluster.