GPU VPS Hosting with a Dedicated NVIDIA GPU
Run AI inference, model training, image generation and CUDA workloads on a VPS with its own NVIDIA GPU, full root access and unmetered bandwidth in a US data center. Billed monthly, with free setup.
AI Powered Hosting Packages
Four GPU VPS plans, from a Quadro P1000 for light CUDA work to an RTX PRO 4000 with 24 GB of VRAM for larger models.
Express GPU VPS
- GPU
- Quadro P1000 · 2 GB VRAM
- vCPU
- 8 cores
- RAM
- 16 GB
- Storage
- 120 GB SSD
- Bandwidth
- 100 Mbps unmetered
- 1 dedicated IPv4
- Backup every 4 weeks
- US data center
Basic GPU VPS
- GPU
- RTX 5060 · 8 GB VRAM
- vCPU
- 16 cores
- RAM
- 28 GB
- Storage
- 240 GB SSD
- Bandwidth
- 500 Mbps unmetered
- 1 dedicated IPv4
- Backup every 4 weeks
- US data center
Professional GPU VPS
- GPU
- RTX A4000 · 16 GB VRAM
- vCPU
- 24 cores
- RAM
- 28 GB
- Storage
- 320 GB SSD
- Bandwidth
- 1 Gbps unmetered
- 1 dedicated IPv4
- Backup every 2 weeks
- US data center
Advanced GPU VPS – RTX Pro 4000
- GPU
- RTX PRO 4000 · 24 GB VRAM
- vCPU
- 24 cores
- RAM
- 56 GB
- Storage
- 320 GB SSD
- Bandwidth
- 1 Gbps unmetered
- 1 dedicated IPv4
- Backup every 2 weeks
- US data center
Which GPU VPS Fits Your Workload?
GPU memory (VRAM) decides which models fit, so start there. CPU, RAM and storage grow with each plan.
| Plan | GPU | Good for | Price | Order |
|---|---|---|---|---|
| Express GPU VPS | Quadro P1000 · 2 GB VRAM | Learning CUDA, light GPU compute, video encoding and small computer-vision models. | $45/mo | Order Express GPU VPS |
| Basic GPU VPS | RTX 5060 · 8 GB VRAM | Stable Diffusion image generation, and small LLMs such as 7B–8B models with 4-bit quantization. | $139/mo | Order Basic GPU VPS |
| Professional GPU VPS | RTX A4000 · 16 GB VRAM | SDXL, LLMs up to about 13B with 4-bit quantization, and LoRA fine-tuning of smaller models. | $179/mo | Order Professional GPU VPS |
| Advanced GPU VPS – RTX Pro 4000 | RTX PRO 4000 · 24 GB VRAM | Larger quantized LLMs (around 30B at 4-bit), longer contexts and heavier inference APIs. | $289/mo | Order Advanced GPU VPS – RTX Pro 4000 |
Typical guidance only. Whether a model fits depends on its size, quantization, context length, batch size and framework.
GPU VPS Servers at Affordable Pricing
Run AI training, inference, notebooks and GPU containers on a dedicated NVIDIA GPU with full root access. Choose your operating system and deploy with JupyterLab, CloudPanel or the GPU Container Runtime pre-installed.
Supported Operating Systems
Deploy your GPU VPS on the Linux distribution your team already knows.
- Ubuntu22.04 LTS · 24.04 LTS · 26.04 LTS
- AlmaLinux8 · 9
- Debian11 · 12
Logos are trademarks of their respective owners. Debian logo © Software in the Public Interest, Inc., CC BY-SA 3.0.
Deploy with Pre-installed Applications
Start working the moment your server is ready. Each GPU VPS can be deployed with JupyterLab, CloudPanel or the GPU Container Runtime already installed.
JupyterLab
JupyterLab is the open-source, browser-based environment from Project Jupyter for notebooks, code, terminals and data. Explore datasets, prototype models and visualise results right next to the GPU, without setting up a local workstation.
- Notebooks, code editor and terminal in one browser tab
- Run Python and other Jupyter kernels on your server
- Ideal for experiments, data exploration and model prototyping
CloudPanel
CloudPanel is a free server control panel for hosting PHP, Node.js and Python applications, static sites and reverse proxies. Publish the API or web app in front of your model with a domain, free Let’s Encrypt SSL and a database, from one dashboard.
- Sites, databases and users managed from a web dashboard
- Free Let’s Encrypt SSL certificates
- Runs on Ubuntu and Debian
GPU Container Runtime
The NVIDIA Container Toolkit lets Docker and other container runtimes use the server’s NVIDIA GPU. Run GPU-accelerated container images, such as framework images with CUDA libraries, and keep every project’s dependencies isolated.
- GPU access inside Docker containers
- Package each model with its own CUDA and framework versions
- Move the same image between development and production
How They Relate
The three work at different layers and are often used together: the container runtime gives your workloads the GPU, JupyterLab is where you write and test code, and CloudPanel publishes the finished app with a domain and SSL.
| Component | Layer / role | Main purpose |
|---|---|---|
| JupyterLab | Development interface | Write and run notebooks and code interactively in a browser |
| CloudPanel | Server management | Manage websites, apps, databases, SSL and server resources |
| GPU Container Runtime | Container infrastructure | Give containers access to NVIDIA GPUs and CUDA |
How Our GPU Pricing Compares
Published list prices for comparable single-GPU plans. Our GPU VPS plans include a dedicated NVIDIA GPU, unmetered bandwidth and a dedicated IPv4 in US data centers, with no setup fee.
| Provider / plan | GPU (VRAM) | Type | Published price | Per month, 24/7 | Setup fee | US data center |
|---|---|---|---|---|---|---|
| OwnWebServersProfessional GPU VPS | NVIDIA RTX A4000 (16 GB) | GPU VPS | $179/mo | $179 | Free | Yes |
| OwnWebServersAdvanced GPU VPS – RTX Pro 4000 | NVIDIA RTX PRO 4000 (24 GB) | GPU VPS | $289/mo | $289 | Free | Yes |
| HetznerGEX45 | NVIDIA RTX PRO 4000 Blackwell SFF (24 GB) | Dedicated server | €214.00/mo excl. VAT | As published | €209.00 | No (EU only) |
| DigitalOceanGPU Droplet | NVIDIA RTX 4000 Ada (20 GB) | Virtual (cloud) | $0.76/GPU/hr | ≈ $555* | None | No (Toronto only) |
| Lambda1x Quadro RTX 6000 | NVIDIA Quadro RTX 6000 (24 GB) | Virtual (cloud) | $0.69/GPU/hr + sales tax | ≈ $504* | None | Yes |
Competitor prices are list prices from each provider’s official pricing page, checked October 21, 2026, and may have changed since. *Hourly prices × 730 hours, our calculation for running 24/7; hourly plans cost less if run part-time. CPU, RAM, storage and bandwidth differ between plans. Sources: Hetzner, DigitalOcean, Lambda.
Why Our US Data Center Network Delivers Better Performance
Four enterprise US data centers in New Jersey, Florida and California keep latency low, websites fast and services available. Our enterprise-grade infrastructure provides reliable connectivity, redundant systems, and scalable resources, with new locations planned as we expand.
GPU VPS FAQs
Common questions about GPU VPS plans: GPUs and VRAM, operating systems, pre-installed apps and billing.
A GPU VPS is a virtual private server with an NVIDIA GPU attached. You get root access to your own Linux server, plus a GPU for CUDA workloads such as AI inference, training and image generation.
No. Every GPU VPS plan includes a dedicated NVIDIA GPU for your server.
VRAM decides which models fit. As a rough guide, 8 GB runs Stable Diffusion and 7B–8B LLMs with 4-bit quantization, 16 GB handles SDXL and models up to about 13B, and 24 GB fits quantized models of around 30B. The exact fit depends on the model and its settings.
A GPU VPS gives you a fixed share of a server’s CPU, RAM and storage with a dedicated GPU, at a lower monthly price. A GPU dedicated server gives you the whole machine, with more powerful GPUs such as the RTX 5090.
GPU VPS plans run in our US data centers.
You can deploy Ubuntu 22.04, 24.04 or 26.04 LTS, AlmaLinux 8 or 9, or Debian 11 or 12.
Your GPU VPS can be deployed with JupyterLab for notebooks and interactive code, CloudPanel for managing websites, apps, databases and SSL, or the GPU Container Runtime (NVIDIA Container Toolkit) for running GPU-accelerated containers.
CloudPanel runs on Ubuntu and Debian. If you want CloudPanel pre-installed, choose one of those operating systems.
It is the NVIDIA Container Toolkit, which lets Docker containers use the server’s NVIDIA GPU. You can then run GPU-accelerated container images, such as framework images with CUDA libraries, with each project isolated in its own container.
GPU VPS plans are billed monthly, in advance. Quarterly, semi-annual, annual and biennial terms are also available at checkout.