GPU as a Service means using GPU servers that sit in a professional data centre and paying a monthly fee instead of buying the hardware. Good GPU server rental is not just a graphics card: it includes power, cooling, networking, monitoring and, if you want it, administration. This article explains the access models, what the fee should cover and when buying your own hardware makes more sense.
We write as the operator of a Tier III data centre in Poland and as a Supermicro and NVIDIA partner. For us, GPU as a Service covers the whole path: from a single card for a team testing its first model to multi-node, liquid-cooled clusters. If you are just starting with self-hosted models, read our self-hosted LLM guide for business first, then come back here when you are choosing how to access the hardware.
What GPU as a Service is, and how it differs from the public cloud
In a public cloud you rent a GPU instance by the hour, in a region picked from the provider’s list. That is convenient for short experiments, but under steady load the bill can be hard to predict: you pay for runtime, storage and data transfer, and the availability of specific GPUs in a given region changes over time.
GPU as a Service the way we run it works differently. You get hardware assigned only to you (a whole card or a whole server), in a known location in Poland, for a fixed monthly fee. We size the configuration for your specific model and user count, and when something breaks you talk to an engineer who knows your environment. It is not a single product but several access models that differ in scale and in who owns the hardware.
Access models: from a GPU VPS to colocating your own hardware
VPS with a dedicated GPU and ready-made packages
A virtual machine with a GPU assigned exclusively to it. This is the simplest entry point: a development environment, a pilot, a small language model for one team, speech transcription. The fastest way to start is with the ready-made packages on our DeepSeek GPU packages page, which range from a single 12 GB RTX A2000 to two A40 cards with 96 GB in total. Despite the name, they run any model: Llama, Qwen, Mistral, Gemma or Whisper.
Bare-metal GPU server (up to 8 GPUs)
A whole physical server with no virtualisation layer and no neighbours. Options include servers with NVIDIA RTX PRO 6000 Blackwell cards (up to eight 96 GB cards), HGX B200 systems (8 × 180 GB HBM3e), HGX B300 systems (8 × 288 GB HBM3e) and the Intel Gaudi 3 platform (eight accelerators with 128 GB each). This is the option for multi-user production inference, large models and fine-tuning. Blackwell servers are quoted individually. See our GPU servers page.
Multi-node GPU cluster
When one server is not enough, for training, the largest models or heavy traffic, we join several nodes into a cluster. The network between servers then becomes critical: HGX B300 systems come with integrated ConnectX-8 800 Gb/s adapters, and Gaudi 3 has six 800 GbE ports on the board. Add shared storage, job orchestration and liquid cooling for dense racks.
NVIDIA DGX Spark as a Service (1, 2 or 4 units)
DGX Spark is a compact system with a GB10 Grace Blackwell chip and 128 GB of unified memory. One unit handles inference of models up to 200B parameters and fine-tuning up to 70B. Two units linked with a QSFP cable give 256 GB and models up to about 400B; four units with a 200 GbE switch give 512 GB and models up to about 700B. The limitation is memory bandwidth (273 GB/s), so it is hardware for R&D, prototyping and private inference for a small team, not for serving hundreds of users. The units sit in our data centre and we look after networking and operations. Pricing is a custom quote. Details: NVIDIA DGX Spark as a Service and DGX Spark clusters.
Colocation of your own hardware
You buy a server, from us (Supermicro with NVIDIA GPUs or Intel Gaudi 3) or elsewhere, and we install it in our data centre and provide power, cooling and networking. You can run it yourself or hand administration to us. More on our server colocation page.
| Access model | Hardware owner | Billing | Best for |
|---|---|---|---|
| GPU VPS / ready-made package | us | monthly subscription | pilots, small models, one team |
| Bare-metal GPU server | us | subscription, custom quote | production, large models, many users |
| GPU cluster | us or you | custom quote | training, the largest models, heavy traffic |
| DGX Spark as a Service | us | custom quote | R&D, prototypes, fine-tuning |
| Colocation | you | hardware purchase + colocation fee | long horizon, steady load |
What GPU server rental includes
When comparing offers, do not look only at the GPU model. The card itself is only part of the cost of running AI hardware. With us, the service includes:
- Power in a Tier III data centre with redundant power paths. With cards drawing hundreds of watts, and up to 1,100 W per GPU for the B300, this is a precondition for stable operation.
- Cooling, including liquid cooling. According to Supermicro, an air-cooled rack holds up to 32 B200 GPUs versus up to 96 in a liquid-cooled rack. For dense configurations we offer liquid-cooled racks. We cover this in GPU server cooling: air vs liquid.
- Networking: internet uplink, a private network between your servers and fast interconnects in clusters.
- Hardware monitoring: temperatures, GPU memory errors, power supplies, disks.
- Hardware replacement on failure: in the rental model this is our job, not yours.
- Physical security and ISO 27001, with data staying in Poland and the EU.
- Administration in one of the two modes shown in the table below.
| Area | Root access (self-managed) | Full 24/7 administration |
|---|---|---|
| Data centre, power, cooling, network | us | us |
| Hardware and replacement | us | us |
| OS, drivers, CUDA | you | us |
| Inference engine and models | you | us, within the agreed scope |
| Service monitoring and alert response | you | us, around the clock |
| Backups | you | us |
The scope of full administration is described on our server administration page.
Rent, or buy and colocate?
There is no single answer, but a few criteria narrow it down quickly. We did a detailed total-cost analysis in cloud vs GPU colocation TCO. Here is the short version.
| Criterion | Lean towards renting | Lean towards buying + colocation |
|---|---|---|
| Project horizon | short or uncertain | several years, clear direction |
| GPU utilisation | variable, with idle periods | high and steady |
| Upfront spend | none | hardware purchase |
| Changing hardware | easy to change configuration or GPU generation | committed for the depreciation period |
| Failure risk | with the provider | with you, under the manufacturer’s warranty |
| Accounting | operating expense | capital expense and depreciation |
In practice a staged approach works well: rent first to validate the model, the real load and user adoption. Once the load is predictable and high most of the time, buying with colocation usually comes out ahead. Many organisations combine the two: a steady baseline on owned hardware, with experiments and peaks on rented capacity.
How getting started works
- Discuss the task: which model, how many users, how long the context, what data and which regulatory requirements.
- Size the configuration: we calculate memory using the method in how much VRAM does an LLM need and propose hardware with headroom for growth.
- Test run on a GPU package or DGX Spark, using your sample data.
- Target environment: the model is exposed through an OpenAI-compatible API, so your applications do not depend on specific hardware.
- Operations: monitoring, updates and scaling as the number of users grows.
Hardware: a Supermicro and NVIDIA partner
Being a Supermicro and NVIDIA partner means we can rent you the same hardware we can sell you and host in colocation. Supermicro offers more than 20 systems for RTX PRO 6000 cards, including a 5U platform for eight cards, HGX B200 and B300 systems in air-cooled and liquid-cooled versions, and the SYS-822GA-NGR3 system with Intel Gaudi 3. On the NVIDIA side this also includes DGX Spark with a software stack covering NIM, NeMo and PyTorch.
As a result, moving from rented to owned hardware does not require redesigning your environment: the owner of the server changes, not the architecture. We always confirm the availability of specific configurations and lead times in the quote.
FAQ: frequently asked questions
How much does GPU server rental cost?
Our ready-made GPU packages have fixed monthly prices listed on the packages page. Blackwell GPU servers, clusters and DGX Spark as a Service are quoted individually, because the price depends on the number of GPUs, networking and the scope of administration.
What is the difference between a GPU VPS and a bare-metal GPU server?
A GPU VPS is a virtual machine with a dedicated card, good for getting started and for smaller models. A bare-metal server is a whole physical machine with several GPUs and no virtualisation layer, intended for production and large models.
Where is my data stored with GPU as a Service?
In our ISO 27001-certified Tier III data centre in Poland, inside the EU. Data is not passed on to third-party cloud providers.
Do I get root access to a rented GPU server?
Yes, if you want to manage the server yourself. Alternatively, choose full 24/7 administration and leave the OS, drivers, inference engine and monitoring to us.
When is it cheaper to buy a GPU server than to rent one?
When you have a multi-year horizon, you know which model you will run, and the GPUs will be busy most of the time. With uncertain load and a short project, renting usually works out better.
Do you offer liquid cooling for GPU servers?
Yes. For dense GPU configurations we offer liquid-cooled racks, both for rented hardware and for servers in colocation.
Want to match the access model to your project? Take a look at our GPU servers or get in touch: we will propose a configuration, run your model on a test setup and compare the cost of renting with buying and colocating.