01PRM
Inference clusters
One to many GPU servers serving models to every internal application through one gateway.
LLM.co · Private AI & Custom AI DevelopmentOn-premises private AI
GPU servers in your data center or colocation cage, running open-weight models behind your firewall. We size the hardware, install the stack and hand over the runbooks.
FIG. — Rack aisle, raised floorLLM.co
On-premises is the most private and, at steady volume, usually the cheapest way to run AI. You buy the hardware once instead of paying per token forever, and no prompt or document ever crosses your perimeter.
We handle the parts that make it hard: sizing GPUs to your models and users, power and cooling checks, the serving stack, monitoring, and the playbooks your team needs to run it.
| GPUs | NVIDIA H200, H100, L40S, RTX 6000; AMD MI300X |
|---|---|
| Serving | vLLM with continuous batching and quantization |
| Gateway | One OpenAI-compatible API, with quotas, routing and keys |
| Monitoring | GPU, latency and quality dashboards; alerts to your on-call |
| Egress | None required for operation |
What we build
What this looks like in practice.
02PRM
Department appliances
A single server for one team, such as legal, engineering or finance, with its own models and data.
03PRM
Training nodes
Hardware for fine-tuning and evaluation that keeps training data inside the building.
04PRM
Hybrid fallback
On-prem first, with policy-controlled overflow to your private cloud when demand spikes.
What an on-premises AI deployment includes
On-premises AI means the GPU servers, the open-weight models and every piece of data they touch sit in a room you control. That can be your data center, a server room or a colocation cage under your contract. An on-premise LLM answers requests from internal applications over your own network and needs no outbound connection to run.
A typical deployment has four layers. Hardware and drivers sit at the bottom. A serving layer, usually vLLM, loads the models and batches requests. A gateway handles keys, quotas, routing and audit logs. On top sit the applications, such as knowledge search, agents or document extraction, built through custom AI development around your workflows.
When on-premises beats private cloud
On-premises AI is usually the right target in these situations.
Private cloud is often the better start when usage is unproven, when you want a pilot running quickly, or when nobody on staff wants to manage physical servers. Many clients start there and move on-premises once the numbers justify it. The stack is the same in both places.
- Usage is steady across many teams, so GPUs stay busy through the working day.
- Policy or contracts keep sensitive data out of any public cloud, including your own tenancy.
- You already run a data center with spare power, cooling and rack space.
- Finance prefers hardware it can own and depreciate over an open-ended usage bill.
Sizing GPU servers for an on-premise LLM
Model size drives GPU memory. Concurrent users and context length drive how much extra memory the key-value cache needs. Throughput targets decide how many GPUs serve in parallel. We measure these against your evaluation set, then write a bill of materials covering GPUs, system memory, NVMe storage and networking.
Smaller models and quantized versions of larger ones run well on cards such as the RTX 6000 or L40S. Larger models, long contexts and many concurrent users call for data-center GPUs such as the NVIDIA H100, H200 or AMD MI300X. We also check rack power, cooling and floor loading before you order, because dense GPU servers draw far more power than typical application servers.
The security model inside your perimeter
An on-premises AI system is governed like any other internal service. Users sign in through your SSO. The gateway enforces roles and document-level permissions mirrored from the source systems, and it logs every prompt, retrieved passage, answer and tool call to your SIEM. The servers sit on network segments you define, with no outbound route required for operation.
Model weights are versioned files on your storage. A model changes only when your team deploys the change, and the previous version stays on disk for rollback.
Running it after handover
Your operations team receives runbooks, dashboards for GPU use, latency and answer quality, and alerts routed to your on-call. Model upgrades follow the same pattern as other software releases. Test the new model against the evaluation set, compare scores, then promote it or keep the current one.
LLM.co can run the system under a support agreement or train your staff to own it. Either way, the hardware, the models and the data remain yours, and the private AI stack has no license that depends on us.
How it works
Four steps, each one reviewed.
01
Size
Model sizes, concurrent users and context lengths turned into a GPU, memory and storage bill of materials.
02
Procure
Vendor-neutral specs and quotes; we work with the reseller you already use.
03
Install
OS, drivers, serving stack, gateway, monitoring and backups, configured to your standards.
04
Hand over
Runbooks, alerts and training for your operations team, plus optional ongoing support.
Questions
Common questions.
What is the difference between on-premises AI and private cloud AI?
On-premises AI runs on GPU servers you own, in a building you control. Private cloud AI runs on dedicated GPU instances in your own AWS, Azure or Google Cloud account. Both keep data away from model vendors. On-premises removes the cloud provider from the picture and usually costs less at steady volume.
What hardware does an on-premise LLM need?
It depends on model size, concurrent users and context length. A single server with RTX 6000 or L40S cards can serve one department with a mid-sized or quantized model. Larger models and organization-wide use call for NVIDIA H100 or H200, or AMD MI300X. We write a vendor-neutral bill of materials from your evaluation results.
Do we need a data center to run AI on-premises?
Not necessarily. A well-ventilated server room or a colocation cage is enough for many deployments. GPU servers draw more power and produce more heat than typical servers, so we check circuit capacity, cooling and rack space during sizing, before any hardware is ordered.
When does on-premises AI cost less than cloud APIs?
Usually once usage is steady and broad across teams. Per-token pricing grows with every request, while owned hardware is a fixed cost spread over its working life. We model your expected token volumes against both options and show the break-even month before you buy anything.
Which models can we run on our own servers?
Open-weight models such as Llama, Qwen, Mistral, DeepSeek, Gemma and gpt-oss, plus embedding and speech models. We pick by bake-off against your evaluation set and quantize where it keeps scores intact, so the chosen models fit the hardware you plan to buy.
Is an on-premise LLM secure?
It keeps prompts, documents and logs inside your perimeter, which removes third-party exposure. The rest depends on the build. Every deployment includes SSO, role- and document-level permissions, audit logging to your SIEM, network segmentation you define, and no outbound route required for operation.
Who runs the system after launch?
Your team, with our runbooks, dashboards and training, or LLM.co under a support agreement. Either way the hardware, models, code and data are yours. Upgrades go through your change control with evaluation scores attached.
Will it work with our existing applications?
Yes, in most cases. The gateway exposes one OpenAI-compatible API, so internal tools and many commercial products that support that interface can point at your own servers. Custom applications we build connect to your ERP, CRM, EHR or document systems directly.
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