dsx-ai-factory/nvke
NVIDIA Kubernetes Engine (NVKE). A management plane for GPU Kubernetes on bare metal.
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SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
3 releases in the last 90 days, the latest 2 days ago, with 68 contributors on GitHub.
Use Cases & Benefits
- Provides a management plane to create and operate Kubernetes clusters on NVIDIA GPU bare metal infrastructure.
- Enables isolated workload clusters with independent control planes and lifecycle management tailored for GPU hardware.
- Use for deploying GPU-accelerated Kubernetes clusters on bare metal for AI and HPC workloads.
- Use for platform teams managing multi-tenant Kubernetes clusters with secure node identity and lifecycle control.
- Use for evaluating Kubernetes GPU cluster management locally without requiring physical NVIDIA hardware.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
- Contributing guide: present
- Code of conduct: present
Watch-outs
No watch-outs from GStars' checks.
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From the README
Run a managed Kubernetes service on your own NVIDIA GPU infrastructure. NVIDIA Kubernetes® Engine (NVKE) provides the management plane that platform teams install to create and operate Kubernetes clusters for their customers. Each workload cluster has its own Kubernetes control plane, etcd and node pools. You operate the NVKE management plane; your customers use their clusters through the Kubernetes API and
kubectl.Preview: NVKE is available as a preview. APIs, configuration and behavior can change between releases, and we do not recommend NVKE for production workloads. See Preview status and known limitations.
The local quickstart runs on a Linux host or VM, amd64 or arm64, with at least 6 CPUs, 12 GiB of free memory and 100 GiB of free disk. It requires Docker, virtual TPM support and the tools listed in the guide. Follow its setup through workload-cluster creation and confirm the cluster's nodes report
Ready. On macOS, use the local site guide, which runs the evaluation inside a Colima virtual machine.For a real installation, you supply the machines, networking, node images and registries, and configure access to NVIDIA Infra Controller (NICo). NICo is the sole compatible infrastructure provider at preview. The platform and version reference lists validated combinations and pinned dependencies.
Compared with peers in Graphics
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| heygen-com/hyperframes | 79.6K | +4.1K | 45 | today |
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| NVIDIA/cuda-rust | 4.0K | +122 | 60 | 4 days ago |
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| processing/p5.js | 24.2K | — | 65 | 7 days ago |
| dsx-ai-factory/nvke | 79 | — | 65 | 2 days ago |
| Category median | 61.8K | +463 | 60 | — |
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