GPU servers for running AI models on your terms
We arrange and manage GPU servers for private AI, inference and fine-tuning work.
In plain English.
Some businesses cannot send their data to a public AI service, or want to run an open-weight model such as Llama, Mistral or Qwen under their own control. That needs a GPU server, with enough graphics memory to hold the model and the right drivers to run it.
We arrange GPU servers through trusted providers, either dedicated machines or cloud GPU instances, and manage the software side: NVIDIA drivers and CUDA, inference servers such as vLLM or Ollama, an API your apps can call, and access controls so only your systems can reach it.
GPU time is expensive, so we start by checking whether a smaller model, a quantised version or an on-demand instance would do the job. GPU servers are quoted after a free call, with the provider's charges and our management shown separately.
It suits you if.
- Your data cannot be sent to a public AI service
- You want to run an open-weight model under your own control
- You need to fine-tune a model on your own examples
- Your AI usage is high enough that API costs are adding up
What we do.
Right GPU for the model
We size graphics memory to the model and context length you need, so you do not rent more than the job requires.
Inference stack
vLLM, Ollama or Text Generation Inference serve the model through an OpenAI-compatible API your apps can call.
Private access
The server is reachable only through a VPN or private network, with a separate API key for each app that uses it.
On-demand option
For occasional jobs such as batch processing or fine-tuning, cloud GPUs can be started when needed and stopped afterwards.
Model updates
We test newer model versions on the server and switch over only when they perform well on your own prompts.
How it runs.
- 01
Use case call
We talk through the model, the data, the number of users and whether a GPU server is really needed.
- 02
Size and quote
We pick the GPU, provider and model setup, and agree all costs before ordering.
- 03
Build and test
The server is configured, the model deployed and tested against prompts from your real work.
- 04
Keep it running
We monitor the server, apply updates and review whether the setup still fits your usage.
Questions people ask.
How much does a GPU server cost?
GPU servers cost considerably more than normal hosting, and the price depends on the GPU, the provider and whether it runs all month or on demand. We quote after a free call, showing the provider's charge and our management fee separately, agreed before anything is ordered.
Will an open-weight model be as good as ChatGPT or Claude?
For many focused tasks, such as summarising documents or answering questions from your own files, a well-chosen open model does a good job. For broad reasoning, the leading hosted models are usually stronger. We test with your real tasks so you can judge for yourself.
Do you own the GPU hardware?
No. We arrange and manage GPU servers through trusted hosting and cloud providers. That keeps you flexible, lets us pick the right hardware for your model and avoids tying you to a single machine.
Can we fine-tune a model on our own data?
Yes, where it is the right approach. Often, giving a model access to your documents through retrieval works better and costs less. If fine-tuning is worth it, we prepare the data, run the training on a GPU instance and test the result.
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