Understanding Go AI Inference: What Is Inference? (internals-for-interns.com)

A new series has been launched to explore AI inference, specifically focusing on how developers can run large language models (LLMs) locally using the Go programming language. Known libraries like Yzma and Kronk facilitate this process, allowing Go developers to directly interface with llama.cpp, a C/C++ inference engine that has made running LLMs on standard hardware feasible. This series aims to clarify the mechanics behind model operation, including how model files are structured and how inference is executed without delving into the neural network's internal mathematical workings. The announcement is significant as it highlights the growing trend of local model deployment, which can enhance data privacy and reduce reliance on external APIs. By understanding inference—where a model utilizes pre-trained weights to generate text output through a series of token predictions—developers can better leverage these tools in production environments. Key technical insights will include how model files, structured as GGUF (GGML Universal File), encapsulate weights and architecture, and how the KV cache optimizes the generation process by storing intermediate computation results to expedite text generation without recurring calculations.