01 / The problem
What the system needs to solve
Running models on owned hardware introduces memory, model-size and concurrency constraints that managed APIs usually hide.
02 / The approach
How the pieces fit together
- Select model sizes and quantization settings to fit the available memory.
- Isolate supporting services and expose inference through an application boundary.
- Document configuration, restart procedures and resource constraints.
03 / The tradeoffs
Engineering is a set of choices
Local hosting offers control over deployment and data handling, but hardware limits affect throughput, model choice and operating effort.
04 / Validation considerations
What to test before relying on it
Evaluate representative prompts, sustained memory use and concurrent requests. Record model and quantization settings alongside any benchmark results.