GPU Dedicated Servers
Bare-metal servers with NVIDIA GPUs for LLM inference, model training, Stable Diffusion, 3D rendering and video transcoding. Full hardware access, no virtualization layer, no vGPU sharing. Deployment in 1–4 hours.
Need a different configuration — more VRAM, several cards, a specific CPU or more storage? Contact support and we will assemble it for your workload.
GPU server plans
Prices shown with the annual payment discount. All configurations include NVMe storage, DDoS protection and unmetered traffic.
Which GPU fits your workload
The deciding factor is almost always VRAM, not raw compute — a model that does not fit in memory will not run at any speed.
| GPU | VRAM | Best for | Rough guidance |
|---|---|---|---|
| NVIDIA RTX 2080 Ti | 11 GB | Video transcoding, 3D rendering, computer vision, small quantised models | The cheapest way to get a real GPU. Fine for inference of 7B-class models in 4-bit quantisation. |
| NVIDIA RTX 5070 Ti | 16 GB | Stable Diffusion / SDXL, video generation, mid-size LLM inference | Current Blackwell generation. Comfortable for image and video pipelines. |
| NVIDIA RTX 4000 SFF Ada | 20 GB | Continuous inference workloads, SDXL, professional rendering | Low power draw, built for 24/7 duty rather than burst loads. |
| NVIDIA RTX PRO 6000 Blackwell | 96 GB | Training, fine-tuning, large LLMs without quantisation | The only configuration here that handles 70B-class models in reduced precision and real fine-tuning runs. |
What people run on these servers
LLM inference
Self-hosted Llama, Qwen, Mistral or DeepSeek behind vLLM, Ollama or TGI. Your prompts and your data never leave your machine — the usual reason teams move off hosted APIs.
Image and video generation
Stable Diffusion, SDXL, ComfyUI, Flux. A dedicated card means no queue and no per-image billing.
Rendering and transcoding
Blender Cycles, NVENC pipelines, batch video processing. NVENC offloads encoding from the CPU entirely.
Training and fine-tuning
LoRA and QLoRA runs, computer vision models, custom architectures. 96 GB of VRAM removes most memory-related constraints.
Scientific computing
CUDA workloads, molecular dynamics, simulation, numerical modelling.
Remote workstation
GPU-accelerated CAD, 3D and video editing over a remote desktop protocol.
Why bare metal instead of a GPU cloud
Predictable cost
A fixed monthly price instead of per-second billing. A long training run does not turn into a surprise invoice.
Full VRAM available
Cloud providers slice cards with vGPU or MIG. Here the entire card and all of its memory belong to one tenant.
No spot interruptions
Nobody reclaims your instance mid-run because a higher bidder appeared.
Your data stays put
No shared storage layer, no cross-tenant snapshots. Relevant for anything under NDA or GDPR.
Available locations
GPU configurations are available in the following data centres. Choose the one closest to your users or your data.
How to get started
Pick a configuration
Choose by VRAM first — it decides what actually fits. CPU and storage matter less for most GPU workloads.
Pay
Crypto, SBP or card. Payment is confirmed automatically.
Get access
Root credentials and IPMI details arrive by email within 1–4 hours. Ask support and we will pre-install the CUDA stack.
Frequently asked questions
Is the GPU dedicated or shared?
Are CUDA and the drivers pre-installed?
Which model sizes fit in memory?
Can I run several GPU cards in one server?
Can I use it for mining?
How long does deployment take?
Can I upgrade to a bigger GPU later?
Not sure which card you need?
Describe your workload to support — model, framework, expected load — and we will tell you which configuration fits and whether a cheaper one would do the job.
Renting a GPU server: what to know before you order
How a GPU server differs from a regular dedicated server
A conventional dedicated server relies on the CPU, which executes a small number of complex operations at a time. A graphics card works the opposite way: thousands of simpler cores run in parallel. That architecture is what makes GPU hosting the default choice for neural networks, since training and inference come down to matrix multiplication — a task that parallelises almost perfectly.
This does not mean a GPU helps with everything. Web applications, databases and most business logic gain nothing from a graphics card. Renting a GPU server makes sense when your workload actually uses CUDA: machine learning, rendering, video encoding, or scientific computing.
What a server with a graphics card costs
The price is driven almost entirely by the card. An entry-level RTX server starts at a few hundred dollars a month; a professional configuration with 96 GB of VRAM costs several times that. CPU, memory and storage shift the total far less than the GPU does.
Compared with buying hardware outright, renting removes the upfront cost and the risk of the card becoming obsolete. A new NVIDIA generation ships roughly every two years, and a purchased card loses value with it. Renting also means someone else handles the failed PSU at three in the morning.
Choosing a configuration for neural networks
Start with video memory. A model that does not fit in VRAM will not run, regardless of how fast the card is. Quantisation reduces the requirement — 4-bit and 8-bit formats cut memory use substantially at some cost in quality — but there is a floor below which a given model simply will not load.
Context length and batch size matter too. A long context inflates the KV cache, and on a card with modest VRAM that becomes the binding constraint before the model weights do. If you are unsure, describe the model and framework to support and we will tell you which card fits.
GPU hosting for business
The most common reason companies move off hosted AI APIs is data. Sending customer records, medical data or internal documents to a third-party endpoint is often incompatible with an NDA or with GDPR. Self-hosted inference on a dedicated server keeps everything inside your own perimeter.
The second reason is cost at volume. Per-token pricing is convenient at low usage and expensive at scale; a fixed monthly server becomes cheaper past a certain request volume. Where that break-even sits depends on the model and the traffic, and it is worth calculating before committing either way.