On-Premise LLM Hardware: What You Actually Need

For most business uses, such as answering questions from company documents, drafting and summarising, an on-premise LLM runs well on a single server with one modern GPU carrying enough memory for the chosen model. The key number is GPU memory: it decides which model sizes you can run and how many people can use the system at once. Larger deployments add GPUs as usage grows.

Businesses in professional services, finance, healthcare and engineering increasingly want AI without sending client data to outside providers. Open models have improved to the point where many business tasks no longer need the largest hosted models, which makes on-premise deployment realistic.

The four things that decide hardware

1. Model size

Models are described by their number of parameters: a few billion for small models, tens of billions for medium ones, and far more for the largest. Larger models reason better but need more memory. Many retrieval-based business tasks run well on small and medium models.

2. Quantisation

Quantisation stores the model's numbers with lower precision, which shrinks memory needs substantially with a modest loss of quality. Most on-premise business deployments use quantised models. The right level is chosen by testing accuracy on your own questions.

3. Concurrent users

Each active request needs memory for its context, the text the model is reading and writing. Ten people asking questions at the same moment need more headroom than one. Most internal assistants see far fewer simultaneous requests than total users.

4. Context length

Retrieval systems send the question plus relevant document passages. Longer contexts need more memory per request. Precise retrieval keeps contexts short, which improves both speed and accuracy.

The components

| Component | What matters | Why | | --- | --- | --- | | GPU | Memory first, then speed | Decides which models fit and how many users are served | | CPU | Moderate | Handles retrieval, pre-processing and the application | | System RAM | Generous | Loading models, document indexes, caching | | Storage | Fast SSD | Models, indexes and documents load quickly | | Network | Standard office or data-centre networking | Serving users across the office | | Power and cooling | GPU servers draw significant power | Plan the location, not just the purchase |

Sizing examples

These illustrate the shape of decisions, not exact specifications, which change as models and hardware evolve.

Small team knowledge assistant (up to about 50 users, light concurrent use). A workstation-class machine with one GPU carrying a moderate amount of memory, running a small or medium quantised model. Suitable for policy, HR and document question-answering.

Department or company-wide assistant (a few hundred users). A server with a data-centre or high-memory GPU, running a medium model with room for several simultaneous requests, plus a separate machine or container for retrieval and the application.

Multiple applications and heavier workloads. Two or more GPUs, possibly different models for different tasks, such as a small fast model for classification and a larger one for complex drafting, with load balancing.

Private cloud as an alternative

If buying and housing hardware is impractical, the same open models can run on GPU servers rented in a cloud account your company owns. Data stays in your account, you avoid capital cost, and capacity can scale up or down. Many businesses start in private cloud to prove the use case, then move on-premise once usage is steady.

Before you buy anything

1. Choose the use case and build a test set of real questions.
2. Test candidate models on rented hardware or a trial machine.
3. Pick the smallest model and quantisation that meets your accuracy bar.
4. Estimate concurrent use from how people work, not total headcount.
5. Size hardware with headroom for growth, then buy.

This avoids the two common mistakes: buying far more than needed, or buying hardware that cannot run the model your accuracy test requires.

Running it well

• Monitor GPU memory, response times and errors.
• Keep models and software updated, testing accuracy after each change.
• Back up document indexes and configuration.
• Restrict access by user and department, as you would any system holding sensitive information.

Frequently asked questions

Is on-premise AI as good as the best hosted models?

For broad reasoning, the largest hosted models are still ahead. For focused business tasks using your own documents, well-chosen open models often perform comparably, especially with good retrieval.

Can we start small and upgrade later?

Yes. Design the application so the model can be swapped or scaled without rebuilding it.

Who maintains the system?

Either your IT team with documentation and training, or a support agreement with the partner who built it.

How does this compare in cost with hosted APIs?

With steady, high usage, on-premise can cost less over time. With light or unpredictable usage, hosted or private cloud is usually cheaper. See what it costs to build an LLM application.

Plan your private AI deployment

Turbo Bytes Consulting designs and deploys custom, private LLM systems on your own servers or cloud account, and builds the AI applications that run on them. Try our knowledge portal demo to see the kind of system this hardware supports.

Book a 30-minute scoping call and we will size a deployment for your use case.