Cloud AI services are convenient, but they come with a condition many organisations cannot accept: the data has to leave the building. For government agencies, defence, healthcare, finance and industry, that is often a deal-breaker.
On-premise AI, running models on your own hardware, offers another path. This article explains when it makes sense, what hardware it needs and how to plan a deployment.
What is on-premise AI?
On-premise AI means running AI models, such as large language models, computer vision and analytics, on servers and workstations that you own and control, inside your own facility or data centre. Data never leaves your network, and nothing depends on an external service being available.
When on-premise AI makes sense
Data sovereignty and confidentiality
If your data is classified, regulated or commercially sensitive, keeping it on your own infrastructure is the simplest way to stay in control. Many organisations must also show where data is stored and processed to meet national or industry regulations.
Air-gapped and secure environments
Some networks are deliberately disconnected from the internet. Cloud AI is simply not an option there; on-premise AI is.
Predictable cost
Cloud AI is billed by usage, which can become expensive and hard to forecast at scale. Owning the hardware turns that into a fixed investment.
Customisation
Running your own models lets you fine-tune them on your own documents, terminology and processes, producing answers that fit your organisation far better than a general-purpose service.
Inference and fine-tuning
Two kinds of AI workload drive hardware choices.
- Inference is using a trained model to answer questions, summarise documents or analyse images. It needs enough GPU memory to hold the model and enough performance to serve your users quickly.
- Fine-tuning adapts an existing model to your data. It is more demanding, needing more GPU memory, fast storage and longer run times.
Many organisations start with inference on an existing open model, then add fine-tuning once they understand how AI fits their work.
Sizing the hardware
GPU memory
GPU memory decides which models you can run. Larger models generally give better results but need more memory, and fine-tuning needs considerably more than inference.
Number of users
A single analyst running a model on a workstation needs far less than an AI assistant serving hundreds of staff. Estimate how many people will use the system at once and how quickly they expect a response.
Storage and networking
Training data, model files and results all need fast, reliable storage. High-throughput storage keeps GPUs busy instead of waiting for data.
Workstations or servers?
- AI workstations suit individual researchers, developers and small teams.
- AI servers suit shared services, larger models and fine-tuning, typically installed in a data centre or server room.
Security still matters on-premise
Keeping data in-house is only the first step. Protect the AI system like any other critical asset:
- Control who can access models and data.
- Encrypt data at rest, ideally at the storage layer.
- Log and monitor usage.
- Keep software and models up to date.
Planning a deployment
- Identify the use case – document search, report drafting, image analysis or something else.
- Choose a model – open models now cover a wide range of tasks.
- Size the hardware for the model, the number of users and any fine-tuning.
- Integrate with your existing systems, data and security controls.
- Train users and gather feedback to improve the results.
Frequently asked questions
Is on-premise AI as good as cloud AI?
For many tasks, yes. Open models have improved rapidly, and fine-tuning on your own data can make an on-premise model more useful for your specific work than a general cloud service.
Can on-premise AI run without the internet?
Yes. Once the model and software are installed, an on-premise AI system can run fully air-gapped.
What hardware do we need for on-premise AI?
It depends on the model size, the number of users and whether you plan to fine-tune. A workstation may be enough for a small team; shared services usually need dedicated GPU servers.
Exploring on-premise AI?
Our Pixelspace AI platform and AI infrastructure solutions help organisations run and fine-tune AI on their own hardware. Talk to our engineers about sizing a system for your needs.