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Ollama Operator 0.10.10, published by Neko, is a Kubernetes controller that streamlines the deployment and life-cycle management of large language models by wrapping the Ollama runtime in custom resources. Designed for DevOps and MLOps teams who want to serve LLMs inside existing clusters without hand-building containers or wrestling with Python dependencies, the operator exposes a concise CRD-based workflow: install the controller, apply the supplied CustomResourceDefinitions, declare model objects, and let the reconciler fetch, cache, and load the weights automatically. Because the underlying inference engine is llama.cpp, binaries are self-contained and GPU acceleration is negotiated transparently, eliminating the traditional hassles of maintaining CUDA drivers or virtual environments. The same declarative pattern supports horizontal scaling, multi-model collocation, and rolling upgrades, so a single cluster can host several concurrent LLM services—each with its own compute quota, token limits, and ingress rules—while respecting Kubernetes RBAC and namespace isolation. Typical use-cases range from internal chatbots and localized LangChain agents to CI-gated A/B testing of foundation models and on-demand AIGC pods for content generation. Since its first release the component has evolved through five documented versions, progressively adding CRD validation, Helm packaging, and Prometheus metrics. The software is available for free on get.nero.com, with downloads provided via trusted Windows package sources such as winget, always delivering the latest version and supporting batch installation of multiple applications.
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