GPU Server Rental in Kazakhstan
NVIDIA H100, H200, A100 and L40S for AI training, inference and rendering.
Renting removes the two problems that come with buying a GPU server: a large upfront payment and hardware that sits idle between projects. You take the card for the weeks you actually need it and release it afterwards. Our servers run in a Kazakhstan data centre, which matters when your data cannot legally leave the country, and every machine is handed over with CUDA and your framework already installed. We size the configuration around the workload rather than selling the largest card in stock.
What you get
Cards matched to the workload
Training a large language model, serving inference at scale and rendering 3D scenes need different hardware. We decide after looking at your model and deadline, not before.
Data stays in Kazakhstan
Servers sit in a data centre inside the country. For banks, healthcare and public-sector work this is usually a hard requirement rather than a preference.
Hourly or monthly billing
Short experiments are billed by the hour, long training runs move to a monthly rate. No year-long contract for a two-week experiment.
Ready environment on delivery
CUDA, cuDNN, PyTorch or TensorFlow, Docker and Jupyter installed and tested before handover, so your team starts the same day.
Frequently asked questions
How quickly can we start?
Standard configurations are handed over within one to three business days. If the card you need is reserved, we say so straight away and offer the nearest alternative.
Is renting cheaper than buying?
Below roughly half-time utilisation, renting almost always wins — idle hardware still depreciates. For a card running continuously for years, buying can be cheaper, and we will tell you which case you are in.
Can we scale from one card to a cluster?
Yes. Most teams start with a single GPU and move to a multi-card node once the model outgrows it. We plan that path in advance so the migration is not a rebuild.
Talk to an engineer, not a sales script
Tell us the workload — model size, number of camera streams, deadline — and we will come back with a configuration and a price. If renting is the wrong answer for your case, we will say so.