Best for:
- GPU container testing
- Small inference workloads
- Learning how to use the NVIDIA Container Toolkit
- Low-cost development environments
NVIDIA Blackwell-based GPU VPS for NVIDIA Container Toolkit & Docker Workloads
DatabaseMart GPU VPS instances are optimized for NVIDIA Container Toolkit, Docker, and CUDA-based workloads. There are no driver conflicts, no CUDA version guessing, and no hardware maintenance requirements. Just create and launch your GPU VPS, start deploying containers to leverage cloud GPU power to run resource-intensive AI image and video generation workflows.
Affordable Blackwell GPU VPS Server Pricing
Powered by the latest Nvidia's Blackwell architecture.
New Arrival
Basic GPU VPS - RTX 5060
$ 85.00/mo
1mo3mo12mo24mo
Order Now- 30GB RAM
- 16 CPU Cores
- 240GB SSD
- 200Mbps Unmetered Bandwidth
- Once per 4 Weeks Backup
- OS: Linux / Windows 10/ Windows 11
- GPU: Nvidia GeForce RTX 5060
- CUDA Cores: 4,608
- Tensor Cores: 144
- GPU Memory: 8GB GDDR7
- FP32 Performance: 23.22 TFLOPS
New Launch Offer
Professional GPU VPS- RTX Pro 2000
$ 95.20/mo
20% OFF Recurring (Was $119.00)
1mo3mo12mo24mo
Order Now- 30GB RAM
- 16 CPU Cores
- 240GB SSD
- 300Mbps Unmetered Bandwidth
- Once per 2 Weeks Backup
- OS: Linux / Windows 10/ Windows 11
- Dedicated GPU: Nvidia RTX Pro 2000
- CUDA Cores: 4,352
- Tensor Cores: 5th Gen
- GPU Memory: 16GB GDDR7
- FP32 Performance: 17 TFLOPS
New Launch Offer
Advanced GPU VPS- RTX Pro 4000
$ 159.00/mo
20% OFF Recurring (Was $199.00)
1mo3mo12mo24mo
Order Now- 60GB RAM
- 24 CPU Cores
- 320GB SSD
- 500Mbps Unmetered Bandwidth
- Once per 2 Weeks Backup
- OS: Linux / Windows 10/ Windows 11
- Dedicated GPU: Nvidia RTX Pro 4000
- CUDA Cores: 8,960
- Tensor Cores: 280
- GPU Memory: 24GB GDDR7
- FP32 Performance: 34 TFLOPS
New Year Sale
Advanced GPU VPS - RTX 5090
$ 278.38/mo
38% OFF Recurring (Was $449.00)
1mo3mo12mo24mo
Order Now- 90GB RAM
- 32 CPU Cores
- 400GB SSD
- 500Mbps Unmetered Bandwidth
- Once per 2 Weeks Backup
- OS: Linux / Windows 10/ Windows 11
- Dedicated GPU: GeForce RTX 5090
- CUDA Cores: 21,760
- Tensor Cores: 680
- GPU Memory: 32GB GDDR7
- FP32 Performance: 109.7 TFLOPS
New Launch Offer
Advanced GPU VPS- RTX Pro 5000
$ 261.75/mo
25% OFF Recurring (Was $349.00)
1mo3mo12mo24mo
Order Now- 60GB RAM
- 24 CPU Cores
- 320GB SSD
- 500Mbps Unmetered Bandwidth
- Once per 2 Weeks Backup
- OS: Linux / Windows 10/ Windows 11
- Dedicated GPU: Nvidia RTX Pro 5000
- CUDA Cores: 14,080
- Tensor Cores: 440
- GPU Memory: 48GB GDDR7
- FP32 Performance: 66.94 TFLOPS
New Launch Offer
Enterprise GPU VPS- RTX Pro 6000
$ 479.00/mo
20% OFF Recurring (Was $599.00)
1mo3mo12mo24mo
Order Now- 90GB RAM
- 32 CPU Cores
- 400GB SSD
- 1000Mbps Unmetered Bandwidth
- Once per 2 Weeks Backup
- OS: Linux / Windows 10/ Windows 11
- Dedicated GPU: Nvidia RTX Pro 6000
- CUDA Cores: 24,064
- Tensor Cores: 852
- GPU Memory: 96GB GDDR7
- FP32 Performance: 126 TFLOPS
Key features:
- Reliable GPU performance
- Optimized for Docker GPU containers
- Affordable entry point for GPU VPS rental
Pro 2000 VPS Server
Entry-Level GPU VPS for Lightweight GPU Containers. Ideal if you want to experiment fast without upfront hardware cost.
Best for:
- Stable Diffusion (basic to medium models)
- PyTorch / TensorFlow inference
- Long-running containerized services
Key features:
- Better performance-to-price ratio
- Suitable for continuous GPU usage
- Smooth experience with NVIDIA Container Toolkit
Pro 4000 VPS Server
Balanced Performance for Daily GPU Workloads. A solid choice for developers and startups.
Best for:
- Heavier AI models
- Multiple concurrent GPU containers
- Professional rendering pipelines
Key features:
- Strong compute performance
- Stable under sustained GPU load
- Designed for containerized production workloads
Pro 5000 VPS Server
High-Performance GPU VPS for Production Containers. Built for serious GPU work without bare metal complexity.
Best for:
- 3D design & rendering
- Large AI models and datasets
- Visualization and simulation workloads
Key features:
- Professional-grade GPU performance
- Large GPU memory capacity
- Ideal for remote workflows without display output
Pro 6000 VPS Server
Professional GPU VPS for 3D, AI, and Visualization. Perfect for designers, researchers, and advanced AI users.
Best for:
- Cutting-edge AI training
- Large-scale inference
- High-resolution rendering
- Performance-critical container workloads
Key features:
- Massive compute performance
- Excellent CUDA and AI acceleration
- Ready for next-gen containerized workloads
RTX 5090 VPS Server
Next-Generation GPU Power for Extreme Workloads. Choose this if performance is your top priority.
Best for:
- Stable Diffusion & image generation
- AI inference workloads
- Video processing & creative applications
- Developers who want newer GPU architecture at a reasonable cost
Key features:
- Newer-generation GPU architecture
- Strong performance for inference and media workloads
- Excellent balance between price, power, and efficiency
- Fully compatible with NVIDIA Container Toolkit & CUDA containers
RTX 5060 VPS Server
Modern, Cost-Efficient GPU VPS for AI Inference & Creative Workloads. Perfect for users who want modern GPU performance without paying flagship prices.
Why Choose DatabaseMart's GPU VPS with Blackwell GPUs?
Built for NVIDIA Container Toolkit
Perfect for Real-World GPU Use Cases
Every GPU VPS comes with:
- NVIDIA drivers pre-installed
- CUDA versions matched and tested
- Docker + NVIDIA Container Toolkit ready (optional)
- docker run --gpus all works out of the box (optional)
- GPU-accelerated microservices
You spend time on models and containers, not infrastructure.
Install Docker and NVIDIA Container Toolkit on Ubuntu 22.04 / 24.04
Below is a clean, simplified version assuming NVIDIA driver is already installed. You can follow the steps below to install NVIDIA Docker yourself, or you can add a note to your order requesting us to pre-install it for you.
Step 1: Install Docker Engine
sudo apt update
sudo apt install -y ca-certificates curl gnupg lsb-release
sudo mkdir -p /etc/apt/keyrings
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | \
sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg
echo \
"deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] \
https://download.docker.com/linux/ubuntu \
$(lsb_release -cs) stable" | \
sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
sudo apt update
sudo apt install -y docker-ce docker-ce-cli containerd.io \
docker-buildx-plugin docker-compose-plugin
sudo systemctl enable docker
sudo systemctl start dockerOptional: allow running Docker without sudo
sudo usermod -aG docker $USER
newgrp dockerStep 2: Install NVIDIA Container Toolkit (NVIDIA Docker)
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | \
sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
curl -fsSL https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt update
sudo apt install -y nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart dockerStep 3: Verify NVIDIA Docker Installation
docker run --rm --gpus all nvidia/cuda:12.4.0-base-ubuntu22.04 nvidia-smiFAQ: GPU VPS with NVIDIA Blackwell GPUs
This FAQ answers the most common questions about using NVIDIA Blackwell GPUs on a GPU VPS, especially for Docker and NVIDIA Container Toolkit workloads.
What are NVIDIA Blackwell GPUs?
NVIDIA Blackwell is the next-generation GPU architecture designed for accelerated computing, generative AI, and high-performance workloads. It succeeds previous architectures and brings major improvements in AI training, inference performance, and energy efficiency.
Which GPU VPS plans include NVIDIA Blackwell GPUs?
Our NVIDIA Blackwell-based GPU VPS plans include Pro 2000 VPS, Pro 4000 VPS, Pro 5000 VPS, Pro 6000 VPS, RTX 5090 VPS and RTX 5060 VPS. These plans are optimized for modern AI, rendering, and containerized GPU workloads.
Are NVIDIA Blackwell GPUs supported by NVIDIA Container Toolkit?
Yes. NVIDIA Container Toolkit fully supports Blackwell GPUs through updated NVIDIA drivers and compatible CUDA runtimes. Docker containers can access Blackwell GPUs using standard commands like --gpus all.
Is CUDA backward-compatible on Blackwell GPUs?
Yes. Blackwell GPUs support newer CUDA versions and maintain backward compatibility for most existing CUDA-based containers. For optimal performance, newer CUDA images are recommended.
What workloads benefit most from Blackwell GPU VPS?
Blackwell GPU VPS is ideal for large AI model inference, generative AI, multi-container GPU workloads, high-resolution image and video generation, and performance-critical CUDA applications.
Is GPU memory dedicated on Blackwell GPU VPS?
Yes. Each Blackwell GPU VPS comes with dedicated GPU memory allocated to your instance, ensuring workload isolation and consistent performance.
How fast can I start using a Blackwell GPU VPS?
In most cases, you can deploy a Blackwell GPU VPS in just a few minutes. If needed, NVIDIA drivers, CUDA, Docker, and the NVIDIA Container Toolkit can be pre-installed, allowing you to run your first GPU container within 15 minutes.
What is the main advantage of using NVIDIA Blackwell GPUs on a VPS?
The main advantage is time-to-value. You gain immediate access to next-generation GPU performance without hardware investment, driver maintenance, or long-term infrastructure commitments.