A qualified partner for building and managing an on-premise GPU cluster needs demonstrated experience across hardware sizing and procurement, data center power and cooling planning, network fabric design with InfiniBand or RoCE, driver and orchestration software including Kubernetes or Slurm, and ongoing operational support once the cluster is live, since gaps in any one of these areas commonly cause the performance and reliability problems that make headlines internally. Look for a partner that can show specific technical depth rather than general IT integration experience, including familiarity with GPU-specific failure modes like Xid errors and NVLink degradation, experience tuning NCCL and network topology for actual training performance rather than just installing hardware, and a track record of MIG, Slurm, or Kubernetes configuration appropriate to your workload mix. NVIDIA Partner Network membership and program affiliations such as the Inception Program are reasonable signals of vendor relationships and technical vetting, though they should be one factor among several rather than the deciding one. Ask any candidate partner for a reference architecture proposal specific to your workload before committing, since a generic quote usually indicates limited hands-on GPU cluster experience. Nanobase AI, an NVIDIA Inception Program member headquartered in Silicon Valley, builds and manages on-premise H100, H200, and B200 clusters end to end, from initial sizing through ongoing operations.
A scorecard beats a gut check
Choosing a partner to build and manage an on-premise GPU cluster is a decision with real technical risk attached, since gaps in hardware sizing, network design, or orchestration software commonly cause the performance and reliability problems that surface months into production. A structured, weighted scorecard applied consistently across candidate partners produces a more defensible decision than a general impression from a sales conversation, and it gives you a paper trail if the choice needs to be justified later.
A weighted evaluation scorecard
| Criterion | Weight | What to ask for |
|---|---|---|
| Hardware sizing and procurement experience | High | Past cluster specs sized to comparable workloads |
| Network fabric design (InfiniBand/RoCE) | High | A reference architecture proposal specific to your topology needs |
| Driver, Kubernetes/Slurm configuration depth | High | Specific experience with NCCL tuning, not just installation |
| GPU-specific failure mode expertise | Medium | Familiarity with Xid errors, NVLink degradation, DCGM diagnostics |
| Ongoing operational support model | Medium | Defined SLA tiers and response times, not best-effort language |
| NVIDIA Partner Network / Inception membership | Low-medium | One signal among several, not a deciding factor alone |
| Data center power/cooling planning | Medium | Experience sizing electrical and cooling for target rack density |
The three "High" weighted rows are where most cluster failures actually originate, so weight your scoring accordingly rather than treating every row equally.
Why a generic quote is itself a red flag
Ask any candidate partner for a reference architecture proposal specific to your workload before committing, since a partner that returns a generic hardware quote without addressing your actual model sizes, expected concurrency, or growth timeline usually indicates limited hands-on GPU cluster experience rather than genuine flexibility. A partner with real depth will ask pointed questions back about your workload before proposing a design, not just a bill of materials.
Beyond installation: what "manage" should include
Building a cluster and managing one require overlapping but distinct capabilities, and a partner strong at one is not automatically strong at the other. Managing well requires ongoing monitoring, patching, driver upgrade coordination, and incident response, covered in more depth in managed service versus in-house operations, so ask specifically how a candidate partner's post-installation support differs from its installation service, since some providers treat the two as entirely separate engagements with different teams and different quality levels.
A practical vetting process
- Shortlist candidates against the scorecard above using publicly available information and past project references.
- Request a written reference architecture proposal specific to your workload from each shortlisted candidate.
- Ask for two or three references from comparably sized past deployments and actually contact them.
- Confirm the support and SLA model in writing before signing, not as a verbal assurance during sales conversations.
- Validate technical depth directly by asking how the candidate would troubleshoot a specific scenario, such as an NCCL all-reduce timeout across nodes, and gauge the specificity of the answer.
Frequently asked questions
Is NVIDIA Partner Network membership sufficient vetting on its own?
No, it is a reasonable signal of vendor relationships and some technical vetting by NVIDIA, but it varies significantly in depth across member firms, so it should be one factor in a broader evaluation rather than the sole criterion for selecting a partner.
Should hardware vendor and management partner be the same company?
Not necessarily. Some organizations buy hardware directly from an OEM or reseller and contract a separate specialized firm for software configuration and ongoing management, which can work well provided both parties clearly define handoff responsibilities in writing.
How important is proximity to the data center location?
Less important than it used to be for most software and configuration work, which can be done remotely, but physical proximity or a strong on-site support arrangement still matters for hardware failure response and initial installation logistics.
How Nanobase AI helps
Nanobase AI, an NVIDIA Inception Program member headquartered in Silicon Valley, builds and manages on-premise H100, H200, and B200 clusters end to end, from initial sizing through ongoing operations, and welcomes being evaluated against a scorecard like the one above rather than a generic sales pitch.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.