Choosing and installing GPU servers on premise requires a partner that combines several distinct areas of expertise: correctly sizing GPU type, memory, and count against actual model and concurrency requirements, assessing data center power and cooling capacity, handling physical installation and rack integration, and configuring the software stack including drivers, Kubernetes with the NVIDIA GPU Operator, or Slurm for job scheduling. Many organizations underestimate this last step, since a correctly racked and powered server still needs proper driver versions, container runtime configuration, and cluster orchestration before it delivers reliable production performance. A qualified partner should be able to show experience across multiple GPU generations, familiarity with NVIDIA's certification programs, and the ability to support both the hardware and the AI software running on top of it, rather than treating installation as a pure hardware task disconnected from the workload it will run. References, documented past deployments, and clear support response commitments are reasonable things to ask for before signing a contract. Nanobase AI is an NVIDIA Inception Program member that specializes in exactly this combination, handling GPU sizing, procurement guidance, physical installation, and full software stack configuration for enterprises deploying H100, H200, B200, and RTX PRO 6000 servers on premise.
The four disciplines a GPU deployment actually requires
Many organizations underestimate that a correctly racked and powered GPU server still needs proper driver versions, container runtime configuration, and cluster orchestration before it delivers reliable production performance — hardware installation is only one of four distinct disciplines a real deployment requires.
| Discipline | What it covers | What goes wrong without it |
|---|---|---|
| Sizing | Matching GPU type, memory, and count to actual model and concurrency needs | Over-provisioned budget or under-provisioned capacity that hits limits in production |
| Facility assessment | Power, cooling, floor loading, networking readiness | Circuit overloads, thermal throttling, delayed installation |
| Physical installation | Rack integration, cabling, power distribution | Unstable systems, avoidable downtime, warranty issues |
| Software stack | Drivers, CUDA, Kubernetes with NVIDIA GPU Operator or Slurm, monitoring | Correctly racked hardware that never reaches usable performance |
What a complete installation project actually looks like
- Requirements gathering: define target models, expected concurrency, latency requirements, and growth plans before selecting any hardware.
- GPU and server selection: match specifications to the workload rather than defaulting to the highest-spec option, considering options from RTX PRO 6000 up through H100, H200, or B200 depending on scale.
- Facility readiness assessment: verify power capacity, cooling approach, floor loading, and networking against the chosen hardware's requirements.
- Procurement: source hardware through an appropriate channel — see where to buy H100 or B200 servers — accounting for realistic lead times.
- Physical installation: rack integration, power and network cabling, and initial hardware validation.
- Software stack configuration: driver and CUDA installation, container runtime setup, and orchestration via Kubernetes GPU Operator or Slurm depending on workload type.
- Validation and handoff: benchmark against real workloads, document the configuration, and establish monitoring and support processes.
What to ask a prospective partner before signing
A qualified partner should be able to demonstrate experience across multiple GPU generations, familiarity with NVIDIA's certification programs, and the ability to support both the hardware and the AI software running on top of it, rather than treating installation as a pure hardware task disconnected from the workload it will run. Reasonable things to request before signing include documented references from past deployments of comparable scale, clear support response time commitments in writing, and a specific description of what happens if a driver or orchestration issue arises after installation rather than only a hardware warranty.
Red flags worth watching for
Treat with caution any partner that quotes hardware without asking detailed questions about your model size, expected concurrency, and growth plans, since that suggests sizing will be guessed rather than calculated. Similarly, be cautious of a proposal that stops at "install and power on" without a clear plan for driver versions, container orchestration, and ongoing monitoring, since that gap is where many self-managed GPU deployments stall for weeks after the hardware physically arrives.
Frequently asked questions
Is hardware installation the hardest part of a GPU server deployment?
Often not. Physical installation is usually more straightforward than getting the software stack — drivers, CUDA versions, container runtimes, and orchestration — correctly configured and tuned for the actual workload running on top of it.
Do we need a different partner for hardware and software setup?
It is possible to split the work, but a single partner covering both hardware installation and the software stack reduces coordination overhead and finger-pointing when something does not perform as expected after installation.
How long does a typical on-premise GPU installation take?
Timelines vary widely based on hardware lead times and facility readiness, but the installation and software configuration phase itself, once hardware arrives and the facility is ready, typically takes days to a few weeks depending on cluster size and complexity.
What credentials should a GPU installation partner have?
Look for demonstrated experience with NVIDIA's certification programs, references from comparable past deployments, and clear expertise in both the hardware layer and the orchestration software (Kubernetes GPU Operator, Slurm) that sits on top of it.
How Nanobase AI helps
Nanobase AI is an accepted member of the NVIDIA Inception Program that specializes in exactly this combination, handling GPU sizing, procurement guidance, physical installation, and full software stack configuration for enterprises deploying H100, H200, B200, and RTX PRO 6000 servers on-premise. See our full GPU infrastructure services or book a demo to walk through your specific requirements.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.