Local LLM Inference with ONNX and C#

Deploying large language models locally without dedicated GPU infrastructure is an essential skill for modern developers and AI engineers. This written course guides you through configuring, loading, and executing quantized models directly on CPU using C# and ONNX Runtime. You will develop a clear understanding of LLMOps fundamentals, model quantization techniques, and token management to build responsive local AI applications. What you'll learn: - Understand core LLMOps concepts, model architecture basics, and vocabulary - Configure ONNX Runtime for CPU execution in C# environments - Load and execute 4-bit quantized Phi-3 models locally - Manage tokenization, prompt templating, and context windows effectively - Apply inference optimization techniques for reliable CPU performance - Implement structured output parsing and basic error handling patterns The reading starts with foundational machine learning terminology and runtime principles before moving into code-driven inference pipelines and optimization strategies. This course is designed for developers and technical beginners interested in running language models locally with C#, requiring no prior machine learning experience. Start reading today to bring local language model execution into your C# applications.