Can we start with a small on-premise LLM pilot before scaling?

Yes, starting with a small on-premise LLM pilot before scaling company-wide is the approach most successful deployments take, since it validates real usage patterns and GPU sizing assumptions on a single server before committing to a larger and more expensive rollout. A typical pilot runs one open-weight model on a single GPU, such as an H100 or an RTX PRO 6000 for a lighter workload, serving a specific department or use case for twenty to fifty users over four to eight weeks, with clear success metrics defined upfront rather than left informal. This scale is enough to surface real adoption patterns, actual token volume, and which use cases employees find genuinely useful, data that is far more reliable for sizing a full rollout than upfront estimates alone. A pilot also gives IT and security teams a contained environment to validate access control, logging and integration approaches before those decisions get locked in at larger scale, where mistakes are more expensive to unwind. The main risk with pilots is letting them run indefinitely without a decision point, so setting a fixed evaluation window and specific go or no-go criteria before starting keeps the pilot moving toward a scaling decision. Nanobase AI runs these scoped pilots as the first phase of nearly every on-premise LLM engagement it takes on.

Why the pilot exists: replacing guesses with real data

Every full on-premise LLM rollout rests on assumptions about usage volume, concurrency and which use cases employees will actually adopt, and those assumptions are far more reliable when replaced with real data before hardware for the full rollout is purchased. A scoped pilot on a single GPU, serving a specific department for a defined period with clear success metrics, produces exactly that real usage data, which is far more trustworthy for sizing a full rollout than any upfront estimate based on headcount alone.

Structuring an eight-week pilot

Breaking the pilot into weekly milestones keeps it moving toward a decision rather than drifting without a clear endpoint.

What a pilot is actually built to measure

A pilot is only as useful as the specific signals it is designed to capture, so these five measurements should be defined before it starts, not reconstructed afterward.

1. Real token volume and query frequency per user, not estimated volume.
2. Peak concurrency, how many users query the system simultaneously during actual working hours.
3. Which specific use cases employees adopt versus which ones go unused, often different from what was expected at kickoff.
4. Response quality and latency as experienced by real users on real tasks, not synthetic benchmarks.
5. Access control, logging and integration approaches validated in a contained environment before they get locked in at larger scale.

Setting the boundaries that keep a pilot from drifting indefinitely

The single most common failure mode for pilots is not technical, it is organizational: a pilot that runs without a fixed evaluation window and specific go or no-go criteria tends to just keep running informally, providing neither a clear success story to justify scaling nor a clear failure to justify stopping. Defining the evaluation window and the specific metrics that constitute success or failure before the pilot starts, and holding a real decision meeting at the end of that window, is what keeps a pilot moving toward an actual scaling decision rather than becoming a permanent, unofficial fixture.

Sizing hardware choices for the pilot itself

A pilot typically runs one open-weight model on a single GPU, an H100 for a heavier model or an RTX PRO 6000 for a lighter workload, since the goal at this stage is validating usage patterns rather than achieving production-scale throughput. This keeps the pilot's own cost and complexity low, which matters because the pilot needs to be cheap and fast enough to run even if the go/no-go decision at the end is "no."

Frequently asked questions

How many users should a pilot include?

Twenty to fifty users from a single department is a common range, large enough to produce meaningful usage data and surface real adoption patterns, small enough to keep the pilot's scope and cost contained.

What happens to the pilot hardware if the decision is to scale?

The pilot GPU can often be repurposed as part of the larger production cluster, or kept as a staging environment for testing future model updates, so pilot hardware spending is rarely wasted even after scaling.

What if pilot usage is much lower than expected?

Low usage during a pilot is valuable information in itself, worth investigating before scaling, since it may point to a use case mismatch, an interface or performance problem, or insufficient user training rather than a fundamental flaw in the approach.

Should IT and security be involved during the pilot or only at full rollout?

They should be involved from the start, since a pilot is the ideal contained environment to validate access control, logging and integration approaches before those decisions get locked in at a larger and more expensive scale.

How Nanobase AI helps

Nanobase AI runs these scoped pilots as the first phase of nearly every on-premise LLM engagement it takes on, setting clear success metrics upfront and delivering a documented go/no-go recommendation at the end of the evaluation window. This connects directly to how long a full on-premise LLM deployment takes and scaling a pilot to a company-wide rollout, with more detail in the on-premise LLM deployment guide.

Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.