Scaling an on-premise LLM from a pilot to a company-wide deployment is mainly a capacity and reliability exercise, since the software stack that worked for twenty pilot users rarely needs to change in kind, only in scale. The first step is measuring real usage from the pilot, tokens per user per day, peak concurrency, and which use cases actually got adopted, since that data should drive GPU sizing far more accurately than upfront guesses. Moving from a single GPU server to multiple nodes typically means migrating from Docker Compose to Kubernetes with the GPU Operator, adding load balancing and redundancy, and introducing proper SSO and role-based access control if the pilot skipped them. Rollout usually goes in waves by department rather than all at once, both to manage GPU capacity and to give IT and support teams time to handle the growth in tickets and training requests. Governance also needs to scale alongside infrastructure, since usage policies, an acceptable-use guideline, and a clear escalation path for incorrect or sensitive outputs become necessary once hundreds or thousands of employees have access rather than a trusted pilot group. Nanobase AI, headquartered in Silicon Valley, has taken multiple clients through exactly this pilot-to-company-wide expansion path.
Scale on data, not on a company-wide launch date
The instinct to plan a single company-wide launch date, sized for the full eventual user base from day one, usually produces over-provisioned infrastructure sitting mostly idle for months. The better approach scales capacity in phases tied to measured usage data from the previous phase, since actual tokens per user, peak concurrency, and which use cases get real adoption are impossible to predict accurately before a pilot generates real numbers. A rollout plan built entirely on projections before any real usage exists is a guess dressed up as a plan.
A phased scaling table
Each phase's trigger column matters more than its user-count column, since real adoption signals, not a calendar date, should decide when to invest in the next tier of infrastructure.
| Phase | Typical user count | Infrastructure | Trigger to move to next phase |
|---|---|---|---|
| Pilot | 10–50 | Single GPU server, Docker Compose | Positive adoption signal, clear use cases identified |
| Departmental | 50–300 | 2–4 GPU nodes, basic HA, Kubernetes migration begins | Multiple departments requesting access, concurrency approaching single-node limits |
| Multi-department | 300–1,000 | Multi-node Kubernetes cluster, full HA, dedicated retrieval infrastructure | Consistent usage across the organization, SLA commitments needed |
| Company-wide | 1,000+ | Multi-node cluster sized to measured peak concurrency, redundant across racks, dedicated operations team | N/A, this is the target state, though the same phased mindset applies to future model or feature additions |
What to measure during the pilot to plan the next phase
- Tokens generated per user per day, which directly drives GPU throughput requirements at the next scale.
- Peak concurrent users, not average, since GPU capacity has to handle the busiest hour, not the daily average.
- Which use cases actually got adopted versus which were requested but never used, since sizing for unused features wastes capacity.
- Retrieval quality complaints, which usually indicate the document corpus or chunking strategy needs work before scaling to more users experiences the same problem at higher volume.
- Support burden, how many questions or issues the pilot generated per user, which predicts the operations staffing needed at the next phase.
Measuring peak concurrency rather than total user count is the single most common sizing mistake to avoid, since a large user base with light, staggered usage needs far less GPU capacity than a smaller group all querying during the same one-hour window.
Organizational scaling alongside technical scaling
Technical infrastructure is only one axis of scaling; the other is organizational readiness, training users, and building a support channel for questions a bigger, less self-selected audience will inevitably raise. Skipping this side of scaling is a common reason company-wide rollouts see poor adoption despite technically sound infrastructure, since employees who don't understand what the tool is for or how to use it well tend to abandon it after a first disappointing interaction. A minimum viable team to sustain this is covered under the minimum team needed to run an on-premise LLM.
Frequently asked questions
How long should each scaling phase last before moving to the next?
There is no fixed duration; the right signal is reaching the usage trigger in the table above, which for an actively promoted pilot might take four to eight weeks, and longer for a quieter, opt-in rollout.
Does scaling always mean adding more GPU nodes?
Usually, but not only that; scaling also means adding high availability, expanding the document corpus covered by retrieval, and often adding operations staff, all of which matter as much as raw GPU count for a good company-wide experience.
What is the biggest risk in moving too fast from pilot to company-wide?
Under-provisioned capacity causing slow responses during the highest-visibility rollout period is the most damaging risk, since a poor first experience for a large new user base is harder to recover from than a slower, more measured rollout that arrives already working well.
How Nanobase AI helps
Nanobase AI plans and executes phased scaling for on-premise LLM deployments, using real pilot usage data to size each subsequent phase rather than guessing at company-wide capacity up front. The team handles the infrastructure scaling, GPU nodes, Kubernetes migration, high availability, alongside the organizational rollout work that determines whether a company-wide launch actually gets adopted.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.