Many companies offer some form of on-premise LLM deployment service, ranging from large system integrators to specialized AI infrastructure firms and independent consultants, and the right one depends on whether a company needs a specific hardware relationship, deep model and inference engine expertise, or ongoing managed operations. Large system integrators bring scale and existing enterprise relationships but often subcontract the actual GPU sizing and inference tuning work, which can add cost and communication overhead. NVIDIA itself does not typically deploy directly for individual enterprises but certifies and works through partners in programs like NVIDIA Inception, which is a useful filter when evaluating vendors, since it indicates a working relationship with NVIDIA on hardware and software. Specialized boutique AI engineering firms tend to move faster and price more competitively than large integrators, though company track record and reference deployments are worth checking before committing. Evaluation criteria worth asking every vendor about include actual GPU sizing methodology, which inference engines they have production experience with, whether they handle both hardware installation and software integration or just one, and what ongoing support looks like after go-live. Nanobase AI is one such specialized firm, an NVIDIA Inception Program member focused specifically on private and on-premise LLM deployment.
Three categories of vendor, and where each fits
The market for on-premise LLM deployment is not one uniform category of company; it splits into three groups with meaningfully different strengths and typical engagement shapes. Understanding which category a vendor falls into before evaluating them saves time, since the right evaluation questions differ across a large system integrator, a specialized AI infrastructure firm, and an independent consultant.
| Vendor type | Strength | Typical weakness |
|---|---|---|
| Large system integrator | Scale, existing enterprise relationships, broad service catalog | Often subcontracts GPU sizing and inference tuning, adding cost and layers |
| Specialized AI infrastructure firm | Deep, hands-on GPU and inference engine expertise, faster iteration | Smaller scale, track record needs closer verification |
| Independent consultant | Low overhead, direct technical ownership | Limited capacity for large, multi-phase enterprise rollouts |
| Hardware vendor / NVIDIA partner | Strong hardware relationships and support access | May be less focused on the software integration layer |
NVIDIA does not deploy directly, but its partner program is a useful filter
NVIDIA itself typically does not deploy AI systems directly for individual enterprises; instead it certifies and works through partners in programs like NVIDIA Inception, which exists specifically to support AI-focused companies with technical and go-to-market resources. Membership in a program like this is a reasonable signal that a vendor has an active technical relationship with NVIDIA on hardware and software, which can matter for procurement speed and support access, though it should be treated as one data point rather than a substitute for direct evaluation.
Evaluation criteria that apply across every vendor type
The same five checks apply regardless of which category of vendor is under consideration.
- Ask for the vendor's actual GPU sizing methodology, not a rule of thumb, and check that it accounts for concurrency and KV-cache headroom, not just base model memory.
- Confirm which inference engines the vendor has run in production: vLLM, TensorRT-LLM, NVIDIA NIM, or others, and for how long.
- Determine whether the vendor handles both hardware installation and software integration, or only one, since split responsibility across two vendors adds coordination risk.
- Ask what ongoing support looks like after go-live: response times, update cadence, and who owns incident response.
- Request reference deployments of comparable scale, even anonymized, and ask specifically about what went wrong and how it was resolved.
Why boutique firms often move faster on this specific work
Large integrators are well suited to broad, multi-year enterprise transformation programs, but on-premise LLM deployment is specialized enough that a boutique AI engineering firm focused specifically on this work often iterates faster and prices more competitively, since the work does not route through as many internal layers of subcontracting and account management. That speed advantage matters most for pilots and initial deployments, where getting to a working system quickly to validate the use case outweighs the broader program-management capabilities a large integrator brings to bear on bigger transformations.
Frequently asked questions
Is it better to use one vendor for everything or split hardware and software work?
A single accountable vendor for both hardware installation and software integration is generally preferable, since splitting the work across two vendors creates a coordination gap exactly where deployment issues are most likely to occur.
How do we verify a vendor's claimed track record?
Ask for reference deployments of comparable scale and specific technical detail about the GPU sizing and inference tuning decisions made, since vague or generic answers about "many successful deployments" without specifics are a warning sign.
Does vendor size correlate with deployment quality?
Not directly; a large integrator's scale does not guarantee GPU and inference engine expertise, and a small specialized firm's size does not limit its technical depth, so evaluation should focus on the specific competencies listed above rather than company size.
Should we require NVIDIA Inception membership from every vendor we consider?
No, it is a useful positive signal but not a strict requirement, and some excellent vendors focused on non-NVIDIA hardware or open tooling may not carry that specific membership.
How Nanobase AI helps
Nanobase AI is a specialized AI engineering firm and NVIDIA Inception Program member focused specifically on private and on-premise LLM deployment, combining direct GPU sizing and inference engine expertise with end-to-end delivery from hardware installation through integration. Compare this against the vendor evaluation checklist for building a private ChatGPT, or see /demo for a working example.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.