Self-Hosting Your First LLM

Self-hosting a Large Language Model (LLM) offers benefits like enhanced privacy, predictable costs, and greater customization compared to using third-party APIs. This guide provides a step-by-step playbook for deploying an agent-oriented LLM on a single machine, focusing on practical considerations for teams facing high API bills or data sensitivity constraints. It details which performance benchmarks are most relevant for agentic tasks, such as function calling and instruction following, rather than general knowledge. The process of model quantization is also explained, outlining how techniques like AWQ and GGUF reduce memory requirements and increase inference speed at a manageable cost to accuracy.