What You'll Learn The Quality Gap: Why moving from 8B parameter models to 70B parameter models fundamentally changes the capabilities of local AI, and why the "sweet spot" has finally arrived. Memory Bandwidth Dynamics: How the architectural leap of the RTX 5090 shifts the bottleneck from raw compute to memory subsystems, allowing for sustained high-throughput inference. Software Architecture: The specific role of inference engines like vLLM and PagedAttention in managing the massive memory requirements of 70B models on consumer hardware. Cost and Privacy Calculus: A comparative analysis of running inference locally versus relying on cloud APIs, focusing on long-term operational costs and data sovereignty. Infrastructure Integration: Practical methods for deploying high-performance local models using Docker, FastAPI, and PostgreSQL for production-grade local applications.…