Self-hosting an LLM is no longer a complex research project. This guide provides a practical playbook for deploying a production-grade model on a single GPU machine, addressing key drivers like exploding API costs, sensitive data concerns, and the need for custom AI behavior. The author evaluates models, instance types, and quantizing techniques to build a cost-effective solution. Single machine deployment simplifies setup but scaling is possible later.

Benchmarks like BFCL v3 and τ-bench are critical for evaluating agent-oriented capabilities, while quantizing methods like BF16 and GPTQ significantly impact performance and memory usage.