GPU Server Comparison: What to Check Before Renting
Compare servers by video memory and resource balance — ready configurations with Tesla P4 from €249/mo, bigger builds made to order
Match a GPU server
When comparing GPU servers, the main parameter is video memory: the model and the batch must fit into it entirely, otherwise the card sits idle. The second is balance: a weak CPU, a shortage of RAM or a slow disk will hold back even a fast GPU, because the data cannot reach it in time. The third is the type of workload: training needs memory and bandwidth, inference is more about concurrent requests, rendering is about engine support. We offer ready configurations with NVIDIA Tesla P4 from €249/mo, and for tasks that need a more powerful card, the configuration and price are quoted individually.
Plans
Ready-made GPU servers
TITAN-3 — a Tesla P4 with 256 GB RAM for inference, transcoding and image generation; TITAN-5 — two cards and 512 GB RAM for parallel pipelines. Dedicated servers: payment upfront, provisioning after the payment is confirmed. Need a more powerful card — describe the task and we will quote a build.
More about GPU serversTITAN-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 |
What matters depending on the task
What to look at first when comparing offers with a GPU.
| Task | Critical first | Often forgotten | |
|---|---|---|---|
| Training a network from scratch | Video memory volume and bandwidth | RAM for batch loading and NVMe for the dataset | |
| Fine-tuning and LoRA | VRAM for the weights and the optimizer | Space for checkpoints of every epoch | |
| Model inference in production | Concurrent requests per card | CPU for tokenization and the queue | |
| Image generation | VRAM for the resolution and batch | Disk for models and outputs | |
| GPU 3D rendering | Support for your engine and VRAM volume | Transfer speed of scenes and textures | |
| Video transcoding | The card's hardware codecs | Network and disk for the source files |
The table is a benchmark for comparing market offers, not a list of our plans. The specific card, VRAM volume and price are matched to your task: describe the model and the data volume.
Video memory first
The model, the batch and the activations must fit into VRAM. If they do not, the computation either fails to start or runs with swapping and loses its point.
Then the balance
A GPU only opens up with enough vCPU, RAM and a fast NVMe under the datasets — otherwise the card waits for data.
And full access
Root access and a KVM console: install your own drivers, CUDA and frameworks — TensorFlow, PyTorch, rendering engines — for your pipeline.
How to order a GPU server
Three steps — from describing the task to a working server.
Describe the task
Tell us what you compute — training, fine-tuning, inference or rendering — which model and how much data.
Pick a ready build or your own
If a Tesla P4 is enough — take TITAN-3 or TITAN-5 from the price list. Need a more powerful card — we will match a configuration and quote that exact build.
Pay and receive the server
A dedicated server is provisioned after payment and machine preparation. Unsure about the choice — we will check the configuration against your task before you order.
We will help build the configuration
Describe the model, batch size and dataset volume — we will propose a build and quote the price. If the task fits into 8–16 GB VRAM, the ready TITAN-3 and TITAN-5 with Tesla P4 are the better value; for everything else we build a configuration with a more powerful card.
Part of the work needs no GPU at all: data preparation, classical ML and inference of small models run on the CPU — see which server machine learning needs and the VPS for AI/ML plans.
Match a GPU serverFrequently asked questions
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The ready configurations use the NVIDIA Tesla P4 with 8 GB: one in TITAN-3, two in TITAN-5. For tasks where that is not enough, we build a configuration with a more powerful card individually.
Ready configurations start at €249/mo (TITAN-3: Tesla P4, 256 GB RAM). A build with a different card is quoted individually: the price depends on the GPU model, VRAM and base resources.
Enough for the model, the batch and the activations to fit entirely. Inference of quantized models needs noticeably less than training from scratch — we will help calculate it.
First by VRAM volume against your model, then by the balance of vCPU, RAM and disk speed. A card without data sits idle, so the GPU model alone tells you nothing.
By the graphics card, which takes over matrix computations: training and inference of neural networks, rendering, transcoding. Everything else — OS, root access, disk — works the same.
Yes. You have root access and a KVM console: install your own drivers, CUDA and frameworks — PyTorch, TensorFlow or a rendering engine — for your pipeline.
There is no free trial: a dedicated server is paid for upfront. Describe the model and data volume before ordering — we will help estimate whether the VRAM and resources are enough.
Data preparation, classical ML and inference of small models run fine on a CPU. Such configurations are covered on the page which server machine learning needs.