Essential Concepts and Terminology for Deploying Large Language Models Locally
This article walks through the core concepts needed before deploying a large language model on‑premises, covering weight precision, quantization methods, model packaging formats, inference engines, GPU memory considerations, KV‑cache sizing, sampling strategies, optional extensions such as LoRA and RAG, and a step‑by‑step decision workflow to match hardware, model, and deployment goals.
