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Run LLMs Locally with Ollama & Semantic Kernel in .NET: A Quick Start
- In this post, you’ll learn how to set up Ollama on your machine, pull and serve a local model like llama3.2. and integrate it with Semantic Kernel in a .NET 9 project.
- Ollama is a self-hosted platform for running language models locally. It eliminates the need for external cloud services and offers data privacy, lower costs, and ease of setup benefits.
- Semantic Kernel is an open-source SDK from Microsoft that enables developers to seamlessly integrate AI capabilities into .NET applications.
- The article lists the prerequisites to run Ollama and Semantic Kernel locally in a .NET 9 project.
- The article shares a step-by-step integration guide for running locally hosted AI models.
- The article also offers sample code and output to help understand how local, generative Artificial intelligence works with Ollama and Semantic Kernel.
- Running AI models locally offers a few use cases including prototyping without incurring cloud costs, internal knowledge bases, and edge or offline applications.
- Combining Ollama and Semantic Kernel lays a foundation for building self-contained, high-performance .NET applications that help maintain complete control over environment and reducing both complexity and recurring costs.
- Further experimentation with Ollama and Semantic Kernel is encouraged, along with experimentation with different models in Ollama.
- Upcoming posts will tackle Retrieval Augmented Generation(RAG) to give LLMs context-aware responses sourced from your own data-all running entirely on local infrastructure.
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