Crusoe expands NVIDIA DSX collaboration with open-source NVIDIA Run:ai

Crusoe, an NVIDIA DSX ecosystem partner, today welcomed the open-source release of NVIDIA Run:ai, expanding opportunities for transparency, extensibility, and community participation. NVIDIA Run:ai is an AI workload and GPU orchestration platform within NVIDIA DSX OS, the open, modular software portfolio of the NVIDIA DSX AI Factory Platform.

Crusoe Managed Kubernetes (CMK) is listed as a partner-compatible Kubernetes distribution for NVIDIA Run:ai. Building on Crusoe’s broader collaboration with NVIDIA across the NVIDIA DSX AI Factory Platform, the integration expands Crusoe’s managed AI infrastructure offering with GPU scheduling, resource allocation and workload orchestration. It gives customers a validated deployment path for scheduling workloads and allocating GPU capacity across shared infrastructure, helping them put more capacity to work without having to assemble and manage the underlying Kubernetes and GPU infrastructure themselves.

The open-source release gives customers new opportunities to inspect, extend, and contribute to NVIDIA Run:ai while continuing to use the CMK integration available today.

“Customers want to get more work out of their GPUs without taking on more infrastructure to manage. NVIDIA Run:ai gives teams control over how GPU capacity is shared and scheduled, while CMK handles the underlying cluster operations. We support making NVIDIA Run:ai open source because customers should be able to understand, extend, and help improve the software that determines how their compute is used.”
- Avinash Mandava, Product Manager at Crusoe

Managed GPU orchestration for AI workloads

GPU clusters often serve multiple teams with different workloads and competing demands for compute. Assigning fixed GPU capacity to each team can leave resources idle in one part of an organization while jobs wait elsewhere.

NVIDIA Run:ai provides workload scheduling and resource management on Kubernetes. Platform teams can establish project quotas and priorities, share available capacity across teams, and support training, inference, and interactive workloads. GPU sharing allows suitable workloads to use a fraction of a GPU, while larger workloads can use multiple GPUs across nodes.

CMK provides the managed Kubernetes foundation for these workloads. Crusoe provisions and manages Kubernetes control planes and GPU node pools, integrates GPU, networking, storage, and autoscaling components, and provides infrastructure health monitoring and remediation. By managing components like the NVIDIA GPU Operator, NVIDIA Network Operator, and Crusoe CSI storage driver, CMK reduces the cluster setup and maintenance work customers do before deploying NVIDIA Run:ai.

Crusoe and NVIDIA validated the deployment and representative workloads, including cluster registration, distributed multi-node PyTorch training, fractional GPU sharing, and inference serving. Customers can configure NVIDIA Run:ai for their teams and workloads without assembling and validating the underlying Kubernetes infrastructure themselves.

Building an open GPU ecosystem together

The open-source release of NVIDIA Run:ai gives customers visibility into the software responsible for scheduling their workloads and allocating GPU resources. Organizations can inspect its behavior, extend it for their requirements, integrate it with existing tools, and contribute improvements to the broader community.

Crusoe supports this approach through contributions to open-source GPU infrastructure projects, including NVSentinel and the Crusoe topology provider in NVIDIA topograph. Contributing upstream makes these improvements available beyond individual customer deployments and allows infrastructure providers, developers, and users to improve shared capabilities together.

For Crusoe, open source and managed infrastructure are complementary. Customers should be able to benefit from open software without having to operate every layer of the infrastructure themselves. NVIDIA Run:ai on CMK brings those benefits together: visibility and flexibility in workload orchestration, with managed operations for the Kubernetes and GPU infrastructure beneath it.

Deployment and ongoing compatibility

Get up and running fast: Crusoe's step-by-step guide walks through installing and configuring NVIDIA Run:ai on new or existing CMK clusters. It covers storage, networking, TLS, load balancing, and inference-serving configuration.

As CMK and NVIDIA Run:ai evolve, so does our partnership. Crusoe and NVIDIA are committed to keeping compatibility validation and deployment guidance current.

Ready to bring NVIDIA Run:ai to your teams? Get started on Crusoe Cloud and create a CMK cluster.

Resources

• NVIDIA Run:ai partner-compatible Kubernetes distributions
• Guide to deploying NVIDIA Run:ai on Crusoe Managed Kubernetes
• Original Crusoe Managed Kubernetes and NVIDIA Run:ai announcement