Self-Hosting Llama 3 for Production: Architecture Guide

A reference architecture for deploying open-source LLMs on your own infrastructure, with cost models.

For: Engineering leaders, AI architects, and infrastructure teams considering self-hosted LLMs

What is inside

• When self-hosting wins — TCO crossover analysis vs hosted APIs
• GPU sizing — A100, H100, L40S, MI300X for Llama 3 70B and 405B
• Inference frameworks — vLLM, TGI, Sglang, llama.cpp trade-offs
• Multi-tenant patterns — per-customer model isolation, request batching
• Observability stack — metrics, traces, evals, drift detection
• Hardening — prompt-injection guardrails, output filtering, audit logging
• India and EU residency patterns