By John McClary (john@cloudnatix.com) 03/17/2025

Managing GPUs across multiple cloud environments can be a challenge. Resource fragmentation, scheduling inefficiencies, and manual workload balancing create unnecessary complexity for data scientists and engineers.

With CloudNatix GPU Federation, you can

Unify GPU resources across cluster and CSPs
Simplify job creation
Intelligently distribute your workloads without manual intervention

A data scientist can simply submit a fine-tuning job to CloudNatix, and we automatically find the optimal node to run the job, avoiding fragmentation and improving operational efficiency

How It Works

CloudNatix abstracts away the complexity of managing GPUs across multiple environments by:

✅ Creating a global Kubernetes cluster that serves as a single interaction point
✅ Automatically forwarding jobs to the appropriate GPU worker clusters
✅ Dynamically scheduling jobs based on availability, cost, and efficiency

See It in Action

In our latest GPU Federation demo, we showcase:

CloudNatix federated control plane to accept user’s Kubernetes jobs
Two worker clusters with GPUs—one small, one large
CloudNatix’s intelligent scheduler dynamically distributing jobs across clusters
Automated job reallocation when resources reach capacity
Seamless integration with standard Kubernetes tooling like kubectl

By eliminating the need to manually track where GPUs are available, CloudNatix enables seamless, scalable, and cost-effective AI workload execution.

🚀 Watch the demo to see how CloudNatix can simplify your GPU job workflow today.