AI servers and GPU servers
An AI server is a rackmount system with several GPUs that serves models to many people at once, runs fine-tuning jobs, or provides on-premises compute for a department. It belongs in a server room or data centre, and it is planned with your IT team before anything is specified.
When it fits
The right choice when
- Many people or applications share the same models at the same time.
- It must run around the clock with remote management.
- You have a server room, a rack or colocation space.
- An IT team will own it after handover.
Look elsewhere when
- It has to sit in an office: rack servers are loud and need dedicated cooling.
- One or two people use it; a workstation or tower is simpler and cheaper.
- Your workload is small or occasional; cloud GPUs may cost less.
What decides the configuration
The variables we work through with you before recommending anything.
| Criterion | Why it matters | What we check |
|---|---|---|
| Rack units and GPU density | More GPUs per server means more heat and power in one place. | Rack space, weight limits and how many GPUs you need now and later. |
| Power and redundancy | Multi-GPU servers draw several kilowatts; a failed supply should not stop service. | Circuit capacity, redundant power supplies and UPS. |
| Cooling and airflow | Passive server GPUs depend entirely on chassis airflow. | Room cooling capacity and a chassis validated for the GPUs. |
| PCIe topology | How GPUs connect affects multi-GPU training and large-model serving. | Lane allocation and GPU-to-GPU paths for your workload. |
| Remote management | Someone must be able to power-cycle and diagnose it remotely. | BMC access, monitoring and alerting into your tools. |
| Networking and storage | Users, datasets and vector stores reach the server over the network. | Uplink speed, storage integration and access control. |
Server GPUs need the right chassis
This is the most common and most expensive mistake in the category.
What we need from your facility
Space
Rack units, depth and weight capacity for the chassis and its rails.
Power
Circuits and sockets for the server's supplies, ideally on separate feeds.
Cooling
Room cooling that can remove the heat a multi-GPU server produces.
Network
Uplinks, VLANs and how users and storage will reach the server.
Reference builds
Configuration examples to start from. Every one is validated against your workload before quoting.
Related guides
Common questions
Can a rack server go in our office?
We advise against it. Server fans run fast and loud, and the heat needs dedicated cooling. A tower or departmental server is the office option.
Do we need redundant power supplies?
For a shared service, usually yes: one failed supply should not take the service down. We size them to the GPUs' load.
Will you install it in our data centre?
Installation and deployment are available and scoped per project, including racking, cabling and software setup.
How long does a server take to deliver?
It depends on component availability at the time. We confirm lead times in the quotation rather than promise them in advance.
Plan a GPU server with us
Start with your users, models and facility. We design the configuration with your IT team before quoting.