High-Performance Workload Orchestration for AI.
Run large-scale training and HPC workloads with Slurm-based orchestration — combining efficient job scheduling with scalable, cloud-native infrastructure.
Core Capabilities
Advanced Job Scheduling
Efficiently manage and distribute workloads across large GPU clusters with Slurm’s high-performance scheduler
Slurm + Kubernetes Integration
Combine Slurm’s resource control with Kubernetes’ scalability and automation for modern AI infrastructure
Auto-Scaling Infrastructure
Dynamically scale compute resources based on workload demand, optimising performance and cost
GPU-Ready Clusters
Pre-configured environments with drivers and libraries for AI and HPC workloads, ready out of the box
Use Cases for Slurm
Flexible GPU infrastructure designed to support development, scaling, and deployment of modern AI workloads.
Distributed Model Training
Run large-scale training jobs across multiple nodes with efficient parallel scheduling.
High-Performance Computing (HPC)
Execute compute-intensive simulations and data processing workloads at scale.
Research & Experimentation
Support AI research workflows with flexible, scalable cluster environments.
Multi-Node GPU Workloads
Orchestrate complex workloads across GPU clusters with high efficiency and reliability.