GPU dedicated servers: the whole machine, every GPU
A physical server with Intel, NVIDIA or AMD GPUs that only you use. Every GPU, every processor core and all of the memory and storage work for your training runs, model serving, render queues and video pipelines, with full root access and no virtualization layer unless you install your own.
- A whole physical server, never shared
- Intel, NVIDIA or AMD GPUs
- No virtualization layer between you and the GPU
- Your own hypervisor with GPU passthrough
Choose a plan that fits
Tell us what you need and our team will help you choose the right plan.
Why choose a GPU dedicated server
A GPU dedicated server gives one customer an entire physical machine. Every GPU, every processor core, all of the memory and the full disk throughput work for your workload alone, so training runs, model serving and render queues keep the same pace, and no other customer can slow you down.
You choose the operating system, the drivers and the software stack, or install your own hypervisor and pass GPUs through to your virtual machines. It is the right step when GPU work has outgrown a virtual server.
- Intel, NVIDIA or AMD GPUs, named with every server
- Every GPU in the machine is yours
- No other customer on the hardware
- Linux, Windows Server or your own hypervisor
- Full root access to drivers and firmware tools
What you can expect from our GPU dedicated servers
Every GPU is yours
The GPUs in the machine work only for your workloads, with all of their memory and compute.
Intel, NVIDIA or AMD
Each server names its GPU, processor, memory and storage, so you can match it to your software before you order.
No noisy neighbours
No other customer runs on the hardware, so training and rendering times stay steady from one run to the next.
Your own hypervisor
Install Proxmox VE, VMware ESXi or Hyper-V and pass whole GPUs through to your own virtual machines.
Full root access
Install drivers, CUDA or ROCm, schedulers such as Slurm and your own services the way your team works.
GPU in containers
Give each container the GPUs it needs with the NVIDIA Container Toolkit, or pass AMD and Intel GPUs as devices.
Your data on your hardware
Datasets, model weights and renders stay on a machine that no other customer uses.
One client area
Order, renew and manage the server and its invoices from the same client area as your other services.
Help from our team
Open a support ticket if the server, a GPU or the network needs attention, and follow every reply in one place.
What customers run on GPU dedicated servers
Training and fine-tuning
Fine-tune and train models with PyTorch, DeepSpeed or Hugging Face libraries on GPUs that no one else uses, with datasets and checkpoints on local storage next to them.
- Steady run times without neighbours
- Datasets stay on your hardware
- Schedulers such as Slurm for your team
$ ssh [email protected]
Welcome to Ubuntu 26.04 LTS (GNU/Linux x86_64)
root@vps:~# apt update && apt upgrade -y
0 upgraded, 0 newly installed, 0 to remove.
root@vps:~# ufw allow 443/tcp
Rule added
root@vps:~# systemctl is-active nginx
active
root@vps:~#
Large models for many users
Serve large open models with vLLM or another inference server behind an OpenAI-compatible API, and split the GPUs between models or teams as your traffic needs.
Render nodes and farms
Render with Blender Cycles on CUDA, OptiX, HIP or oneAPI, or run your studio’s render manager on machines that keep going overnight and over the weekend.
Video pipelines and transcoding
Transcode libraries and live streams with FFmpeg on the GPU’s hardware video engines: NVENC on NVIDIA, VA-API on AMD and Intel, and Quick Sync on Intel.
Your own GPU virtualization host
Install a hypervisor and give each virtual machine a whole GPU through passthrough, for separate teams, customers or test environments on one machine.
VM 1
VM 2
VM 3
Research and data science
Give a research group JupyterHub, GPU-accelerated data processing and long experiments on a machine they control, with no queue behind other customers.
Technical specifications
What every server on this page shares. The GPU, processor, memory and storage differ by server and are listed with each one.
- GPU
- Intel, NVIDIA or AMD
- Each server names its GPU
- Server type
- A whole physical server
- No other customer on the hardware
- Virtualization
- None, unless you install your own hypervisor
- GPU passthrough to your own VMs
- Access
- Root over SSH
- Administrator over Remote Desktop on Windows Server
- Containers
- Docker and Podman with GPU access
- NVIDIA Container Toolkit, or /dev/kfd and /dev/dri for AMD and Intel
- GPU drivers
- Installed by you with root access
- NVIDIA driver and CUDA, AMD ROCm or Intel GPU drivers
- Operating systems
- Listed with each server
- Pick one in the order form
- Locations
- Listed with each server
- Pick one in the order form
- Support
- Support tickets from the client area
GPU VPS, GPU VDS or GPU dedicated server?
All three come with Intel, NVIDIA or AMD GPUs and root access. They differ in how much of the GPU and of the machine is yours.
| Feature |
GPU VPS
A virtual server with a GPU
|
GPU VDS
A larger virtual server with nested virtualization
|
This page GPU dedicated server
A whole physical server with its GPUs (Recommended)
|
|---|---|---|---|
| What you get | A virtual server with a GPU | A larger virtual server with a GPU and nested virtualization | A whole physical server with its GPUs |
| GPU | Shared vGPU or dedicated GPU | Shared vGPU or dedicated GPU | Every GPU in the machine |
| GPU makers | Intel, NVIDIA or AMD | Intel, NVIDIA or AMD | Intel, NVIDIA or AMD |
| Your own VMs insideNested virtualization passes the processor’s virtualization extensions to your server, so it can run virtual machines and Android emulators of its own. | Not included | Included | Yes, with your own hypervisor |
| Hardware used only by youA VPS or VDS is a virtual machine on a physical host that also runs other customers' virtual machines. | Not included | Not included | Included |
| Root access | Included | Included | Included |
| Best for | Inference, image generation, notebooks and remote desktops | Android emulators, GPU labs with VMs and larger AI stacks | Training, large models for many users and long render queues |
| Price level | Lowest | Middle | Highest |
-
GPU VPS
A virtual server with a GPU
- What you get
- A virtual server with a GPU
- GPU
- Shared vGPU or dedicated GPU
- GPU makers
- Intel, NVIDIA or AMD
- Your own VMs insideNested virtualization passes the processor’s virtualization extensions to your server, so it can run virtual machines and Android emulators of its own.
- Not included
- Hardware used only by youA VPS or VDS is a virtual machine on a physical host that also runs other customers' virtual machines.
- Not included
- Root access
- Included
- Best for
- Inference, image generation, notebooks and remote desktops
- Price level
- Lowest
-
GPU VDS
A larger virtual server with nested virtualization
- What you get
- A larger virtual server with a GPU and nested virtualization
- GPU
- Shared vGPU or dedicated GPU
- GPU makers
- Intel, NVIDIA or AMD
- Your own VMs insideNested virtualization passes the processor’s virtualization extensions to your server, so it can run virtual machines and Android emulators of its own.
- Included
- Hardware used only by youA VPS or VDS is a virtual machine on a physical host that also runs other customers' virtual machines.
- Not included
- Root access
- Included
- Best for
- Android emulators, GPU labs with VMs and larger AI stacks
- Price level
- Middle
-
This page
GPU dedicated server (Recommended)
A whole physical server with its GPUs
- What you get
- A whole physical server with its GPUs
- GPU
- Every GPU in the machine
- GPU makers
- Intel, NVIDIA or AMD
- Your own VMs insideNested virtualization passes the processor’s virtualization extensions to your server, so it can run virtual machines and Android emulators of its own.
- Yes, with your own hypervisor
- Hardware used only by youA VPS or VDS is a virtual machine on a physical host that also runs other customers' virtual machines.
- Included
- Root access
- Included
- Best for
- Training, large models for many users and long render queues
- Price level
- Highest
A comparison of the three GPU lines, not of single plans: each page lists its plans with the GPU, resources and price.
Set up your GPU dedicated server
Step-by-step guides for GPU drivers, model servers and AI tools, written for current Ubuntu and Debian releases.
-
How to install vLLM on an NVIDIA GPU server with Docker
Prepare an NVIDIA GPU server, run vllm/vllm-openai with Docker Compose on 127.0.0.1, secure it with an API key and a Caddy proxy that only exposes /v1, tune memory and multi-GPU settings, or install it with uv and systemd.
45 min Advanced -
How to install ComfyUI on an NVIDIA GPU server securely
Set up ComfyUI from the official repository on a GPU server, run it under its own user with systemd, keep it off the public internet, add HTTPS with basic authentication, and manage models, custom nodes, backups and updates.
45 min Intermediate -
How to install Ollama on Ubuntu or Debian and run local LLMs
Install Ollama as a systemd service, pull and run open models on CPU or GPU, test the REST API, move model storage and reach the API securely without exposing port 11434.
30 min Beginner -
Dedicated servers at HyperDC: what you get and first checks
A dedicated server is a whole physical machine for you alone. What is included, which operating systems you can run and the hardware checks worth doing on day one.
15 min Intermediate -
How to install JupyterLab on a Linux server with systemd and HTTPS
Set up single-user JupyterLab for a dedicated Linux user: virtual environment, hashed password, a systemd service bound to 127.0.0.1, SSH tunnel or Caddy access, GPU support, backups and updates.
30 min Intermediate -
How to run llama.cpp server as an OpenAI-compatible API
Compile llama.cpp, test llama-server with a GGUF model from Hugging Face, run it as a hardened systemd service on 127.0.0.1 with an API key, and publish the OpenAI-compatible API over HTTPS.
40 min Intermediate
What you get with HyperDC
One account for everything
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Invoices in one place
Every invoice is listed in the client area, where you can also pay it online.
Support from the client area
Open a support ticket whenever you need help and follow every reply in one place.
From order to online
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Choose a service
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Complete your order
Review your cart and pay with one of the available payment methods.
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Receive your details
We email your login details as soon as the service is ready.
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Get help when you need it
Open a support ticket from the client area and follow every reply.
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One client area for every service
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Linux VPSActiveHelp from our team
Open a support ticket from the client area and follow every reply.
Choose your location
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All data centersRoom to grow
Move between hosting, VPS, VDS and dedicated servers as your project grows.
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Tell us what you run today and where it is hosted. We will reply with the steps to move it to your new HyperDC service.
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Services that work well together
Nested Dedicated Servers
Dedicated resources with virtualization extensions for your own VMs.
Learn morePolicies that apply
Frequently asked questions
What is a GPU dedicated server?
A GPU dedicated server is a physical server with one or more GPUs that serves one customer only. Its GPUs, processor, memory, disks and network port are not shared with anyone, so performance stays predictable and you decide how the hardware is used.
How is it different from a GPU VDS with a dedicated GPU?
A dedicated GPU on a VPS or VDS is a whole GPU for your virtual server, on a physical host that also runs other customers’ virtual machines. A GPU dedicated server is the physical host itself: every GPU in it, all processor cores, memory and storage are yours, and you can install your own hypervisor.
Which GPUs do you offer?
Intel, NVIDIA and AMD GPUs. Each server on this page names its GPU, processor, memory and storage, so you can check it against the requirements of your software, for example CUDA for NVIDIA, ROCm for AMD or oneAPI for Intel, before you order.
Can I run my own hypervisor and pass GPUs to virtual machines?
Yes. Install a hypervisor such as Proxmox VE, VMware ESXi or Hyper-V and pass whole GPUs through to your own virtual machines, for example one GPU per team or per workload. Passthrough needs the processor’s IOMMU, which you enable in the server’s firmware settings; our team helps through a support ticket if you need access to them.
Which operating systems can I install?
The systems each server offers are listed on this page and in the order form: Linux distributions for AI and rendering work, Windows Server for graphics tools and remote desktops, or a hypervisor for your own virtual machines. Choose a release your GPU software supports.
How do I install the GPU drivers?
You install them yourself with root access: NVIDIA’s data center driver and CUDA toolkit from NVIDIA’s repositories, AMD ROCm or Intel’s GPU drivers. Our guides for vLLM, Ollama and ComfyUI walk through the NVIDIA setup and the NVIDIA Container Toolkit step by step.
Is a GPU dedicated server right for training models?
Yes. Training and fine-tuning keep GPUs busy for hours or days and read datasets and checkpoints from disk all the time. On a dedicated server no other customer shares the GPUs, the processor or the storage, so run times stay steady and your data stays on hardware only you use.
Can I use the GPUs inside Docker?
Yes. With NVIDIA GPUs, install the NVIDIA Container Toolkit and choose which GPUs each container gets with --gpus; AMD and Intel GPUs are passed to containers as devices such as /dev/kfd and /dev/dri. Images for vLLM, Ollama, ComfyUI and PyTorch then use them.
How long does it take to set up a GPU dedicated server?
It depends on the server and its location. When a server lists a delivery time, that is the time to expect; otherwise we prepare the server after your order is paid and email the login details as soon as it is ready. If you have a deadline, contact us before ordering.
Do you also offer smaller GPU servers?
Yes. GPU VPS and GPU VDS come with a shared vGPU, a share of a physical GPU, or a dedicated GPU, a whole GPU for your virtual server. They are a lighter start for inference, image generation, emulators and remote desktops.
Which billing cycles can I choose?
Every plan lists the billing cycles it is offered with. Switch the cycle above the plans to compare prices; longer cycles show the saving against monthly billing when there is one.
How do I pay?
Choose one of the payment methods offered at checkout. Every invoice also stays available in the client area, where you can pay it online.
Where do I manage my service after ordering?
Your service appears in the client area as soon as it is set up. From there you can manage it, pay invoices and open support tickets.
Questions before you order?
Send us a message and our team will help you choose the right service.