Migrating LLM workloads from the cloud to on-premise starts with sizing the target GPU cluster correctly, typically H100, H200, or RTX PRO 6000 based on model size and expected concurrency, then replicating the serving stack, usually vLLM or TensorRT-LLM, in the new environment before cutting over traffic. The practical sequence is to benchmark current cloud throughput and latency as a baseline, procure and install the on-prem hardware with Kubernetes and the NVIDIA GPU Operator or Slurm for scheduling, deploy the model and validate output parity and performance against the cloud baseline, then shift traffic gradually using a load balancer or feature flag rather than an abrupt cutover. Data pipelines, vector databases for RAG, and monitoring need to be mirrored or migrated alongside the model itself, and authentication or API contracts used by downstream applications should stay unchanged to avoid breaking integrations. Lead time for GPU procurement and data center power or cooling readiness is usually the longest pole in the schedule, often several months, so migration planning should start well before the target cutover date. Nanobase AI, an NVIDIA Inception Program member, runs cloud to on-premise LLM migrations end to end including hardware sizing, installation, and cutover validation.
Why a phased cutover beats a hard switch
The riskiest part of a cloud-to-on-premise migration is not the hardware installation, it is the moment traffic moves from the proven cloud deployment to the new on-premise one. A phased cutover using a load balancer or feature flag to shift a small percentage of traffic first catches parity issues while the cloud deployment is still available as a fallback, whereas an abrupt full cutover discovers problems only after the cloud environment may already be decommissioned or scaled down. This single decision, phased versus abrupt, has more influence on whether a migration causes an incident than any hardware or software choice made earlier in the project.
The runbook, stage by stage
- Benchmark the current cloud deployment's latency, throughput, and output quality on a fixed test set to establish a baseline for comparison.
- Size and procure on-premise hardware, typically H100, H200, or RTX PRO 6000 GPUs, based on the same model and concurrency the cloud deployment currently serves.
- Install Kubernetes with the NVIDIA GPU Operator, or Slurm if the workload is training-heavy, and validate basic GPU scheduling before deploying the model.
- Deploy the serving stack, matching the cloud environment's framework version, whether vLLM or TensorRT-LLM, as closely as possible to avoid unrelated variables.
- Run the same benchmark suite from step one against the on-premise deployment and compare directly against the cloud baseline.
- Mirror data pipelines and any RAG vector store needed by the workload, validating retrieval results match between environments.
- Route a small percentage of production traffic to the on-premise deployment, monitor closely, then increase gradually as confidence builds.
- Decommission cloud capacity only after on-premise has carried full production traffic reliably for a defined observation period.
Each stage exists to catch a specific failure mode before it reaches production traffic, which is why skipping a step to save time usually costs more time later.
What typically takes longer than expected
| Phase | Common estimate | What actually extends it |
|---|---|---|
| Hardware procurement | Weeks to a few months | Supply constraints on the newest GPU generation |
| Data center readiness | Weeks | Power and cooling capacity not sized for GPU density |
| Software parity validation | 1-3 weeks | Subtle output differences between framework versions |
| Traffic cutover | 2-4 weeks | Conservative, staged rollout by design |
The pattern across all four phases is that hardware and facility lead times, not the software migration itself, are usually what push a project's timeline out, so planning should start well before the target cutover date.
The rollback plan, decided before day one
A migration plan without a rollback plan is a plan to improvise during an incident. Before shifting any production traffic, the team should confirm the cloud deployment can be scaled back up quickly if the on-premise environment shows a problem after cutover, which means not decommissioning cloud capacity immediately even after cutover begins. DNS or load balancer configuration should support flipping traffic back to cloud within minutes, not hours, and monitoring thresholds that trigger an automatic or manual rollback should be defined and agreed upon before the first percentage of traffic moves, not decided reactively once an issue appears.
Frequently asked questions
How long does a typical cloud-to-on-prem LLM migration take end to end?
It varies widely by hardware availability and facility readiness, but a realistic range is several months from initial planning to full cutover, driven mainly by GPU procurement lead time and data center power or cooling preparation rather than the software migration itself.
Do output quality differences between cloud and on-prem deployments happen often?
Yes, subtle differences can appear from framework version mismatches, different quantization defaults, or driver version differences, which is why running the same benchmark suite against both environments before cutover is an essential validation step, not an optional one.
Should we migrate all workloads at once or one at a time?
One at a time is generally safer, starting with a lower-risk workload to validate the process before migrating higher-stakes production traffic, since early migrations often surface process gaps that are cheaper to fix on a smaller workload.
What happens to API contracts used by downstream applications during migration?
They should stay unchanged throughout the migration; the goal is that the model backend changes location without downstream applications needing code changes, which is why matching the serving framework's API surface between cloud and on-prem matters.
How Nanobase AI helps
Nanobase AI, an accepted member of the NVIDIA Inception Program, runs cloud-to-on-premise LLM migrations end to end, including hardware sizing, installation, staged cutover, and rollback planning, so a migration does not trade cloud reliability for on-premise risk during the transition. See our on-premise LLM deployment guide for the infrastructure side of this work.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.