How long does it take to deploy a self-hosted large language model?

Where the weeks actually go

The table below assumes a common shape: single-chassis inference, one business line, ten to two hundred internal users. These are empirical ranges for pacing, not a committed schedule. The larger the scope and the more departments involved, the more the upper bound stretches — especially for data governance and compliance sign-off, whose duration tracks organisational coordination cost rather than technical difficulty.

Why "one day to install" and "four weeks to use" are both true

Pulling open weights and serving them with an off-the-shelf inference stack really is a sub-day task, and that is where most timeline intuitions come from. A usable system additionally needs: a decision about which documents are searchable, cleaning and chunking of those documents, a permission model governing who sees what, an evaluation method proving the answers are right, integration with internal identity, monitoring and recovery, and trained users. None of these is hard individually, but each requires alignment with a different group, and together they add up to weeks. A credible implementation plan lists them; a plan that says "model deployment: 1 day, go-live: 1 day" has usually hidden the work the buyer has to do.

Three things that genuinely shorten the schedule

First, run tracks in parallel. Procurement, document cleanup and evaluation-set construction have no mutual dependencies, and serialising them adds weeks for nothing. Data governance can even start before the contract is signed, because it has to happen whichever vendor you pick. Second, shrink phase one. Limiting the first release to one department, one document type and one question class typically halves the time to launch, and expanding after real feedback is cheaper than building everything at once. Third, prototype on a hosted API while the hardware ships: validate retrieval, permissions and the user interface against a public model, then swap only the inference backend when the GPUs land. That assumes the test data is allowed to leave the network — for sensitive data it is not an option.

Where this applies

When this answer does not hold

• These ranges assume single-chassis inference for one internal business line. Shared multi-team platforms, customer-facing high concurrency or multi-node clusters take substantially longer and do not fit this model.
• Without an empowered business owner and a technical counterpart on the buyer side, every estimate breaks down: waiting on decisions and approvals routinely exceeds the implementation itself.
• In classified environments, finance and healthcare, compliance review can far exceed the upper bound here and is not controlled by the project team. Schedule it separately against your own regulator's real cadence.
• If the goal is fine-tuning or continued pre-training rather than inference deployment, the time profile is entirely different: annotation and training iterations dominate, and this phase breakdown does not apply.

Related questions

Related applications