* [NVIDIA GPU Operator v26.7.0](https://github.com/NVIDIA/gpu-operator) – Automates installation and lifecycle management of GPU drivers, container runtimes, and monitoring on Kubernetes nodes.

* [NVIDIA AI Cluster Runtime v0.20.0](https://github.com/NVIDIA/aicr) – Tooling for optimized, validated, reproducible GPU-accelerated Kubernetes clusters via version-locked recipes and deployment-ready bundles.

* [NVSentinel v1.20.0](https://github.com/NVIDIA/NVSentinel) – Cross-platform remediation service detecting, classifying, and automatically resolving runtime GPU node faults in Kubernetes clusters.

* [Kubernetes AI Toolchain Operator (KAITO) v0.12.0](https://github.com/kaito-project/kaito) – Operator automating AI/ML model inference and tuning workloads in Kubernetes clusters with GPU auto-provisioning and large model management.

* [TypeGPU v0.12.2](https://github.com/software-mansion/TypeGPU) – TypeScript library enhancing the WebGPU API for type-safe resource management.

* [NVIDIA Cloud Functions (NVCF) deploy/helm/containe...](https://github.com/NVIDIA/nvcf) – Platform for deploying, managing, and running GPU-accelerated inference, streaming, and batch workloads across worker clusters.

* [wgpu v0.31.6](https://github.com/gogpu/wgpu) – Pure Go WebGPU implementation providing W3C-compliant API and multiple hardware backends without Rust or CGO.

* [GPUd v0.13.0-beta.0](https://github.com/leptonai/gpud) – GPU-focused monitoring and diagnostics tool that detects GPU and fabric errors and reports critical system metrics.

* [I3K RAG Engine v0.1.39](https://github.com/I3K-IT/RAG-Enterprise) – Self-hosted retrieval-augmented generation engine that keeps all document processing and answering offline on a single binary, with cited sources.

* [LLMKube llmkube-0.9.19](https://github.com/defilantech/LLMKube) – Kubernetes operator managing self-hosted LLM inference on NVIDIA GPUs and Apple Silicon, with autoscaling, model routing, and OpenAI-compatible API.