AI Servers

Three different jobs, three different machines

The phrase "AI server" covers at least three configurations, and none of them is the best one. There is the one that fits your job.

• Training and fine-tuning. What decides here is GPU memory and the speed of the link between cards. The 141 GB on an H200 keeps in memory what smaller cards force you to split into pieces.
• Inference under load. The model is already trained, and what matters is concurrent requests per dollar spent. Several RTX PRO cards beat one of the most expensive ones here.
• Local work under the desk. A compact station that keeps the model in-house, sends no data outside and does not roar in an office.

What we ask before an order

How many parameters the model has, what precision you plan to run, and how many users will work at the same time. Those three answers are enough to name the GPU memory and the card count you need.

Then comes placement. A rack in a server room, a pedestal in an office and a shelf in a study set different demands on noise, power and cooling.

What a ready system includes

Every configuration is assembled and tested as a whole: cards, a CPU with enough PCIe lanes, memory sized to the model, drives for datasets, power with headroom for spikes. The system can be adjusted to you, from drive capacity to an InfiniBand adapter.

Describe the task in your own words and we will propose a configuration, explaining why this particular set.