AI and GPU workloads
We choose with you the GPU, memory and storage your workload needs and hand over a server ready to work.
Tell us what is happening: we review your situation and identify what makes sense to assess first.
GPU sized for your AI workload
I need GPUs to train or run models.
Does any of this sound familiar?
- Training or running models on your own machine is slow or impossible.
- You pay for a GPU you barely use, or can't get capacity when you need it.
- You don't know which GPU or how much memory you need.
- Setting up the environment (drivers and libraries) takes days.
- Your data requires a dedicated server rather than a shared service.
How we solve it
GPU servers, dedicated servers, application stacks and server administration.
- 01
Discovery
We understand your model, your data and your way of working.
- 02
Sizing
We define the type and number of GPUs, memory and storage.
- 03
Proposal
We deliver the proposed configuration, with alternatives and lead times.
- 04
Implementation
We provision and configure the server with the agreed environment.
- 05
Operation
We administer and support it, at the chosen level.
What to expect
- Capacity sized to your workload, not to a generic list.
- An environment with drivers and libraries already set up.
- Your data on a dedicated server, not on a shared service.
Services we solve it with
What depends on a technical assessment
GPU model and memory, number of machines, availability, storage and level of administration.
What it does not cover
GPU availability depends on stock: we state it in the proposal. We don't develop or train models.
Frequently asked questions
Can I choose the GPU model?
It is evaluated based on your workload and what is available at the time.
Do you train the model for me?
No: we deliver the infrastructure and its administration.
Tell us what you need to solve
The first conversation is about understanding the situation. You do not need to know the right product in advance.
