Run AI on your own machine: the local-first stack

A local-first stack keeps core state on equipment you control. A model runtime such as Ollama can serve a compatible checkpoint locally, and Xavani can provide the agent workflow around a configured provider. Check current setup instructions for your platform.

Choose hardware using the model's actual memory requirements, quantisation and expected context length. Leave capacity for the operating system and other applications. Test responsiveness under a representative workload before buying a larger machine.

Local inference does not make the whole agent offline. Search, messaging, remote embeddings and hosted fallback models may send information elsewhere. Disable unneeded integrations and verify network behaviour if a workflow must stay offline.

Protect local storage, backups and credentials. Nyarhi can add a local knowledge layer, but retention and access controls remain your responsibility. Local deployment is one way to manage privacy risk, not automatic POPIA compliance.

Open-source software can avoid licence fees while still carrying hardware, electricity, support and maintenance costs. Keep a tested update and recovery process. Ask Enternovate to scope the workflow and machine together rather than choosing hardware from a model headline alone.