There is no fixed price for an on-premise ChatGPT-equivalent, since cost scales with model size, GPU count and how much integration work is involved, but as of 2026 a realistic range for a mid-sized enterprise deployment runs from roughly the cost of a single GPU server into the high six figures for a multi-node cluster serving a large organization, so verify current pricing before budgeting. The GPU hardware itself is usually the largest line item, since a single H100 or H200 server capable of running a well-quantized 70B model can serve a department, while a company-wide deployment with hundreds of concurrent users and a larger model typically needs multiple GPU nodes networked together. Beyond hardware, budget should include the software integration work, connecting the chat interface, SSO, document retrieval and any enterprise system connectors, plus ongoing costs for power, cooling, maintenance and whatever staff or managed service keeps the system running. Smaller pilots can start meaningfully cheaper on a single GPU or even a high-memory workstation for a limited user group before scaling. The total figure depends far more on integration scope and user count than on the model license itself, since most capable open-weight models carry no per-seat fee. Nanobase AI provides project-specific quotes after sizing the actual workload rather than a generic price list.
The model license is rarely the expensive part
Most enterprises budgeting for an on-premise ChatGPT-equivalent instinctively look for a per-seat software price, the way they would for a SaaS tool, but that is the wrong mental model. Most capable open-weight models carry no license fee at all, so the budget is driven almost entirely by GPU hardware, integration engineering and ongoing operations, not by paying for the model itself.
Cost by deployment scale
The right way to think about the number is by deployment scale, since hardware and integration needs both jump at fairly predictable size thresholds.
| Scale | Typical hardware | What it supports | Where cost concentrates |
|---|---|---|---|
| Pilot | Single GPU (RTX PRO 6000 or one H100) | 20-50 users, one department, one use case | Integration time more than hardware |
| Mid-size | One to two H100 or H200 servers | A few hundred users, moderate concurrency | Hardware plus SSO, RAG and connector integration |
| Enterprise-wide | Multi-node H100/H200/B200 cluster | Thousands of users, multiple use cases | Hardware, networking, and ongoing operations staff |
As of 2026, a realistic range spans from the cost of a single GPU server for a contained pilot into the high six figures for a multi-node cluster serving a large organization; verify current GPU and networking pricing before finalizing a budget, since prices shift with supply and generation.
The four line items that make up the real budget
- GPU hardware, sized to the model and concurrency target, usually the single largest line item for mid-size and larger deployments.
- Integration engineering, covering the chat interface, single sign-on, retrieval-augmented generation over company documents, and any connectors into systems like SAP or Salesforce.
- Ongoing infrastructure costs, including power, cooling, networking and datacenter or colocation space if not hosted in an existing server room.
- Operations and maintenance, either internal staff time or an external managed service, covering monitoring, patching and model updates.
Why integration scope moves the number more than model choice
Two organizations deploying the identical open-weight model at the identical GPU count can end up with budgets that differ by a large margin, and the difference is almost always integration scope rather than hardware. Connecting the LLM to a single document repository with basic SSO is a fraction of the engineering effort of wiring it into five enterprise systems with granular role-based access, custom connectors and a change-managed rollout across multiple departments. Budgeting from user count and model size alone, without pricing the integration work explicitly, is the most common way on-premise AI budgets go wrong.
Starting smaller to de-risk the number
A pilot on a single GPU or even a high-memory workstation for a limited group is a legitimate way to validate the use case and get a real usage baseline before committing to a larger cluster. This also produces actual token volume and concurrency data, which sizes the next phase of hardware far more accurately than an upfront estimate based on headcount alone.
Frequently asked questions
Does the model itself ever have a licensing cost?
Most leading open-weight models used in on-premise deployments, including the Llama, Qwen and DeepSeek families, are free to use commercially under their respective licenses, though it is worth checking each model's specific license terms for any usage restrictions.
How much of the budget is hardware versus integration?
For a contained pilot, integration work often dominates since hardware needs are modest; as scale grows, hardware becomes the larger line item, though integration cost grows too if more enterprise systems are connected.
Can we reduce cost by using a smaller quantized model?
Yes, running a 70B model quantized to INT4 rather than FP16 cuts memory needs roughly in half again versus FP8, which can reduce the GPU count needed, though it is worth validating that quality holds up for the specific use case.
Is renting cloud GPUs cheaper than buying for a first deployment?
For a short pilot or uncertain workload, renting can avoid upfront capital risk, and many organizations start there before purchasing on-premise hardware once the use case and expected usage are proven out.
How Nanobase AI helps
Nanobase AI provides project-specific quotes after sizing the actual workload, model choice and integration scope, rather than a generic price list that ignores what actually drives cost. This pairs with detailed hardware sizing guidance and the broader GPU sizing guide for 70B and 405B models.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.