Deploying an on-premise LLM typically takes between four and twelve weeks for a focused pilot, and three to six months for a company-wide rollout with full integration, depending mainly on how much custom connector work and hardware procurement is involved. A minimal proof of concept, one GPU server, an open-weight model, and a chat interface for a small group of pilot users, can be running within two to four weeks if hardware is already on hand, since the software stack itself installs quickly. Timelines stretch significantly when GPU hardware has to be ordered, since H100, H200 or B200 servers can carry lead times of several weeks to a few months depending on supply and configuration. Adding retrieval-augmented generation over company documents, single sign-on integration, and connectors into systems like SAP or Salesforce each add real engineering time, typically two to six weeks per major integration depending on how clean the source systems' APIs are. Company-wide rollouts also need change management, training and a phased department-by-department expansion, which extends the calendar well beyond the technical build itself. Nanobase AI typically scopes a realistic timeline against these variables at the start of a project rather than quoting a generic number.
The timeline is set by hardware lead time and integration count, not software installation
Installing an inference engine and a chat interface on already-available hardware takes days, not weeks. What actually determines the overall project timeline is GPU procurement lead time, which can run several weeks to a few months for H100, H200 or B200 configurations depending on supply, and the number of enterprise integrations layered on top, each of which adds real engineering time of its own.
A representative phase breakdown
Laying out a typical project against its actual phases shows where the calendar time really goes.
| Phase | Typical duration | What happens |
|---|---|---|
| Scoping and sizing | 1-2 weeks | Model selection, GPU sizing, use case definition |
| Hardware procurement | 2-16 weeks | Ordering, shipping and racking GPU servers, highly variable by configuration and supply |
| Base stack installation | 3-7 days | Inference engine, chat interface, model deployment on available hardware |
| SSO and access control | 1-3 weeks | Identity provider integration, role-based access setup |
| RAG over company documents | 2-6 weeks | Document indexing, retrieval tuning, accuracy validation |
| Enterprise connectors (SAP, Salesforce, etc.) | 2-6 weeks per system | API integration, testing, dependent on source system API quality |
| Pilot user rollout | 2-4 weeks | Limited group testing, feedback collection, adjustment |
| Company-wide rollout | 4-12 weeks | Phased department rollout, training, change management |
Why a minimal pilot can move much faster than the table suggests
If GPU hardware is already on hand, either through an existing server or a quickly available cloud rental for validation, a bare pilot consisting of one model, one GPU and a chat interface for a small group can be running within two to four weeks. This path skips the procurement bottleneck entirely and defers the heavier integration work until the use case is validated, which is why many organizations deliberately choose to prototype this way before committing to a larger, longer project.
- Validate the use case on available or rented hardware first, deferring procurement until the use case is proven.
- Order GPU hardware for the production deployment as soon as the use case is validated, since this step has the longest and least controllable lead time.
- Build SSO and basic access control in parallel with hardware procurement rather than after hardware arrives.
- Scope RAG and enterprise connectors by priority, building the highest-value integration first rather than all of them simultaneously.
- Run a bounded pilot with a fixed evaluation window before committing to company-wide rollout timing.
Integration count is the variable most teams underestimate
Each additional enterprise system connected, whether SAP, Salesforce, Microsoft 365 or ServiceNow, adds real engineering time that scales with how clean and well-documented that system's API is, not with how important the integration feels. A project scoped for one document repository and basic SSO can realistically land at the fast end of the range, while the same project with four enterprise connectors added midway will stretch well past initial estimates if that scope was not built into the plan from the start.
Frequently asked questions
Can hardware procurement and software integration happen in parallel?
Yes, and doing so is one of the most effective ways to compress the overall timeline, since SSO integration, RAG pipeline development and chat interface configuration do not require the final production hardware to be in place.
What is the single biggest cause of timeline slippage?
Underestimating integration scope, particularly the number and complexity of enterprise system connectors, is the most common cause, since each connector's timeline depends on a source system's API quality that is often unknown until integration work actually starts.
How much faster is a pilot than a full rollout?
A minimal pilot on available hardware can be running in two to four weeks, roughly four to six times faster than a full company-wide rollout with complete integration, which typically takes three to six months.
Does company-wide rollout timing depend on technical work alone?
No, change management, training and phased department-by-department expansion extend the calendar meaningfully beyond the technical build itself, and this non-technical work is often underestimated in initial project timelines.
How Nanobase AI helps
Nanobase AI scopes a realistic timeline against these specific variables, hardware lead time, integration count and rollout scope, at the start of every project rather than quoting a generic number that ignores them. This pairs well with starting from a scoped pilot before committing to a full timeline, and with the broader on-premise LLM deployment guide. Explore /solutions for engagement structures.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.