Train faster, scale autonomously
Scale without overhead. With Crusoe Managed Kubernetes and Managed Slurm, we handle the provisioning and failover so you can focus on the model, not the machinery.
Model training on Crusoe Cloud
Crusoe Cloud provides managed infrastructure services specifically engineered to satisfy the stringent requirements of contemporary AI workloads. We integrate substantial computational capacity with adaptable orchestration capabilities to guarantee the efficient and dependable completion of your training runs.
Managed Orchestration with Crusoe Managed Kubernetes
Deploy high-performance GPU clusters via Crusoe Managed Kubernetes (CMK) or Crusoe Managed Slurm (CMS) in minutes. Built-in NVIDIA drivers with Quantum-2 InfiniBand networking and full AMD GPU support via ROCm ensure immediate compatibility across hardware configurations with no manual environment setup required.
The scheduler automatically maps jobs to the optimal node topology across your cluster. Native support for PyTorch, Kubeflow, and Ray means distributed training jobs are placed and balanced efficiently, with automated re-queuing maintaining workload continuity across interruptions.
Scale to thousands of GPUs with near-linear efficiency. AutoClusters automates node recovery and job resumption so workloads keep running without manual intervention, while Crusoe Command Center provides real-time cluster health visibility across the full fleet.
run Slurm
run Slurm
Crusoe Managed Slurm clusters come pre-configured with Slurm workload manager, NVIDIA drivers, and job scheduler, so your team can submit jobs immediately without standing up or tuning the environment. Whether you're migrating existing HPC workloads or starting fresh, CMS drops into familiar Slurm workflows with no retraining required.
Crusoe benefits for model training
Crusoe Cloud provides a fully managed environment that allows engineers to focus on code and model development, turning infrastructure into a competitive advantage.