NVIDIA GPU Server Rental — from €249/mo
Rent a server with an NVIDIA GPU for inference, rendering and compute — a Tesla P4 already in the configuration, and we will help you size it right
Launch a GPU server
A GPU server is a dedicated server with an NVIDIA Tesla P4 graphics card (8 GB) and CUDA support. It suits neural network inference, image generation, video transcoding and parallel compute. Plenty of RAM (256–512 GB) and NVMe drives speed up work with large datasets. Ready configurations start at €249/mo; the server is issued after payment. For heavy training we will build a configuration with a more powerful card — describe the task and we will quote the build.
Plans
Servers with NVIDIA Tesla P4
TITAN-3 — one Tesla P4 and 256 GB RAM for inference, transcoding and image generation; TITAN-5 — two cards and 512 GB RAM for parallel pipelines. These are dedicated servers: paid upfront, issued after the payment clears. Need a more powerful card — write to us and we will build it to spec.
How to choose a GPU serverTITAN-3 · GPU
| NVIDIA Tesla P4 · 8 GB |
| AMD EPYC 7452 · 32C/64T |
| 256 GB RAM |
| 3 TB NVMe + SSD + 18 TB HDD |
| Dedicated server · IPMI |
| 24/7 support |
TITAN-5 · 2× GPU
| 2× Tesla P4 · 16 GB VRAM |
| AMD EPYC 7663 · 56C/112T |
| 512 GB RAM |
| 8,7 TB NVMe + 960 GB SSD |
| Dedicated server · IPMI |
| 24/7 support |
An NVIDIA GPU with CUDA
A dedicated GPU with CUDA/cuDNN support for neural network training, inference and parallel compute.
Rendering and video processing
Hardware acceleration for 3D rendering, transcoding and graphics work — with no queues.
Full control of the environment
Root access, an IPMI console and SSH: you install the NVIDIA drivers, CUDA and the frameworks your task needs.
How to launch a GPU server
Choose a configuration
TITAN-3 with one Tesla P4 or TITAN-5 with two — by VRAM, RAM and disk for your models. Need a more powerful card — write to us.
Pay and receive the server
A dedicated server is issued after payment and machine preparation. Before you order, we will help confirm the configuration fits your task.
Set up the GPU stack
Install the OS through the IPMI console, then over SSH — the NVIDIA drivers, CUDA and frameworks (PyTorch, TensorFlow) for your task.
A GPU for your tasks: ML, rendering, compute
Neural network training and inference, 3D rendering, video processing and scientific computing — wherever parallel compute is needed, a GPU delivers a multiple speed-up over a CPU.
We will help pick a configuration for your stack: VRAM, RAM and disk size — describe the task and we will suggest the best fit.
Help me chooseFrequently asked questions
Didn't find your answer?
Write to us via the contact page.
An NVIDIA Tesla P4 with 8 GB of VRAM and CUDA support: one card in TITAN-3, two in TITAN-5. Need something more powerful — describe the task and we will build a configuration for it.
The Tesla P4 works well for inference, LoRA fine-tuning and small models. For training large models from scratch 8 GB of VRAM is not enough — for that we build configurations with a more powerful card.
No free trial: a dedicated server is paid upfront. To avoid a mistake, describe your model and data volume before ordering — we will tell you whether the configuration can handle it.
Yes, you install the NVIDIA drivers and the CUDA version you need yourself. If you run into trouble, we will help with the environment setup.
For inference of 7B models in quantised form — from 8 GB; for training and SDXL — from 16–24 GB. VRAM limits model size harder than regular RAM does.
Yes. Data preparation, code debugging and inference of small models run fine on a regular VPS from €5.49/mo, and a GPU is added only for the training stage.
You choose the image and deploy the OS yourself: the IPMI console gives hardware-level access for installing and reinstalling the system, on top of SSH.
Yes. As the load grows, you can move to a configuration with more RAM, disk and a more powerful GPU — we will help plan the data migration.