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Seeking out a way to ship LLMs more seamlessly? Way too complicated to manage, composite, and setup a runtime with Python, C++, CUDA, GPUs when deploying LLMs? Tired of fighting against dependencies, model sizes, syncing deliverable model images across nodes? It's true that people often find it hard to bundle, distribute, deploy, and scale their own LLM workloads, but no worries, here is Ollama Operator, a scheduler, and utilizer for LLM models powered by Modelfile introduced by Ollama. You can now enjoy then unified bundled, runtime powered by llama.cpp with simple lines of CRD definition or the natively included kollama CLI with single command line, bundling, distributing, deploying, scaling of LLMs can never be easily and seamlessly accomplished across OS and environments. Let's dive in and find out what Ollama Operator with Ollama can do to deploy our own large langaugae models, what can we do and combine these features with Modelfile then bring them into the Kubernetes world!

寻找一种更无缝地运输LLM的方式？在部署LLM时，使用Python、C++、CUDA、GPU设置运行时太复杂？厌倦了与依赖、模型大小、在节点间同步可交付模型图像等问题作斗争？ 人们常常发现很难捆绑、分发、部署和扩展自己的LLM工作负载，但不用担心，这里有Ollama Operator，一个由Ollama引入的基于Modelfile的LLM模型调度器和利用者。现在，您可以通过简单的CRD定义行或内置的kollama CLI命令行，享受由llama.cpp提供支持的统一捆绑运行时，轻松实现LLM的捆绑、分发、部署和扩展，跨操作系统和环境都可以轻松实现。 让我们深入了解一下Ollama Operator与Ollama能够做些什么来部署我们自己的大型语言模型，我们可以如何结合这些功能与Modelfile，然后将它们带入Kubernetes世界！