Free Whitepaper
Local AI That Competes
Compare systems, not models. Whether a self-hosted open-weight model holds up is decided by the context you assemble, the tools it can call, the checks it has to pass and the training data your workflow produces on its own. Christoph Hess sets out five engineering levers and the conditions under which each one pays off.
- Four conditions that decide whether a workload belongs on local inference at all
- Why price per token is the wrong number, and what to measure instead
- Five engineering levers, each with its own decision rule, its own failure modes and its own point of diminishing return