A few years ago, running a capable language model meant renting someone else's hardware. Today a single workstation with a modest GPU can serve models that handle summarisation, code assistance, and document search well enough for daily use.
Local inference changes three things at once. Costs become fixed instead of per request. Data never leaves the machine. Latency depends on your hardware, not on a distant queue.
A practical local stack
Most local setups share the same pieces: a runtime that loads models and exposes an API, a small library of models chosen for specific tasks, and the applications that call that API. Because the API is local, swapping models becomes a configuration change.
- Pick small models for fast, frequent tasks and larger ones for depth.
- Expose the runtime only on the local network.
- Monitor GPU memory, since it is usually the first limit you hit.
The cheapest request is the one that never leaves your network.
Where it is heading
Models keep getting smaller for the same quality, and runtimes keep getting more efficient. The likely future is hybrid: routine work handled locally, with remote models reserved for the rare tasks that truly need them.