Running LLMs locally is increasingly practical, and for businesses with sensitive data it can be the right call. But "local AI" is an infrastructure project, not a download. Here is what to plan for.
Size hardware to workloads, not hype
GPU requirements depend on model size, context length, and concurrency. Start from your real workloads — expected requests, latency targets, and prompt sizes — and size from there. Over-provisioning GPUs is expensive; under-provisioning is frustrating.
Separate serving from application logic
Treat the model server as infrastructure with a stable interface. Keep your automation and application logic separate so you can swap or upgrade models without rewriting everything around them.
Retrieval is where the value lives
For most business use cases, the model matters less than the retrieval layer feeding it. Invest in clean embeddings, a solid vector store, and thoughtful chunking. Good retrieval turns a general model into a useful one.
Plan for operations
Private AI still needs monitoring, updates, backups, and access control. The privacy benefit is real, but it comes with the operational responsibility of running the stack.
Local LLMs reward teams that treat them as production infrastructure from the start.