NVIDIA has released the full NVIDIA Run:ai platform as open source software under the Apache 2.0 license on GitHub. The release puts production GPU scheduling, quotas, and workload management directly into developers' hands, making Run:ai part of the NVIDIA DSX AI Factory platform. Platform teams can now inspect, fork, and self-host the stack without requiring a paid subscription.
The open source platform includes a monorepo featuring a control plane, cluster services, an installer, a CLI, and an MCP server. NVIDIA split the release into four modular components that teams can adopt independently, including the KAI Scheduler, the GPU Fractioning Operator for CUDA-native GPU fractions, KAI Resource Management, and the Workload Map Go library. Developers can run the platform locally using a setup script that builds about 50 Go services inside containers on macOS or Linux without needing physical GPU hardware.
While the community edition lacks NVIDIA enterprise support, teams can deploy it from source or via the NGC Community Product Helm chart alongside ecosystem partners. Furthermore, an adjacent project called Model Streamer ships alongside the release to stream model weights directly into GPU memory, achieving significantly faster loading speeds during testing. This major open-source release matters because it lowers the barrier for developers and enterprises managing complex Kubernetes GPU infrastructure and AI workloads.