A CUDA VPS is a virtual server with an NVIDIA GPU and the CUDA toolkit, so you can run GPU-accelerated code: AI frameworks like PyTorch, TensorFlow and JAX, inference engines like vLLM, or your own CUDA kernels. Here are the best CUDA VPS providers in 2026 and how to set one up.
Table of Contents
Quick Comparison: Best CUDA VPS (2026)
| Provider | Best GPU for This | Starting Price | Billing | Best For |
|---|---|---|---|---|
| GPU Mart | RTX A4000 / RTX 5090 VPS | $21/month | Monthly | Cheap always-on CUDA VPS |
| RunPod | RTX 4090 / H100 | $0.16/hour | Per second | CUDA templates |
| Lambda | A6000 / H100 | $1.09/hour | Hourly | Lambda Stack (CUDA ready) |
| HOSTKEY | RTX 4090 / H100 | €70/month | Hourly & monthly | EU CUDA servers |
| Vast.ai | Any NVIDIA GPU | Market price | Per second | Cheapest CUDA compute |
Best CUDA VPS Providers
1. GPU Mart
- Dedicated GPU servers and GPU VPS (US)
- GPUs from P1000 to RTX 5090, RTX PRO 6000, A100 and H100
- One-click AI apps: Ollama, Stable Diffusion, ComfyUI
- GPU VPS from $21/mo · RTX 4090 $409/mo · RTX 5090 from $419/mo · A100 80GB $1,559/mo · H100 $2,099/mo
Pros
- Lowest monthly prices for dedicated GPUs
- Full root/admin access, Windows or Linux
- Multi-GPU servers available
Cons
- Monthly billing only
- US data centers only
GPU VPS plans with NVIDIA GPUs from $21/month; the RTX A4000 (16 GB) VPS at $119/month is a great CUDA development box.
2. RunPod
- Per-second billing in 30+ regions
- Templates: vLLM, ComfyUI, PyTorch, Ollama, Jupyter
- Serverless AI endpoints
- RTX 3090 $0.22/hr · RTX 4090 $0.34/hr · A100 $1.19/hr · H100 $1.99/hr · H200 $3.59/hr (Community Cloud)
Pros
- Very cheap consumer GPUs
- Huge GPU choice up to B200
- Fast start-up
Cons
- Storage billed when stopped
- Community hosts vary
RunPod templates ship with CUDA, cuDNN and PyTorch pre-installed; pick your CUDA version when you deploy.
3. Lambda
- On-demand GPU cloud built for AI
- Lambda Stack pre-installed
- 1× to 8× GPU instances and clusters
- A6000 $1.09/hr · A100 40GB $1.99/hr · GH200 $2.29/hr · H100 SXM from $3.99/hr · B200 from $6.69/hr
Pros
- Reliable data-center GPUs
- No egress fees
- Simple pricing
Cons
- GPUs can sell out
- No monthly servers
Every Lambda instance includes Lambda Stack: NVIDIA drivers, CUDA, cuDNN, PyTorch and TensorFlow.
4. HOSTKEY
- GPU servers in the EU, UK and US
- RTX 4090, RTX 5090, RTX PRO 6000, A100, H100
- Pre-installed AI stack: Ollama, Open WebUI, ComfyUI
- GTX 1080 Ti from €70/mo · RTX 4090 €279/mo · RTX 5090 €590/mo · A100 80GB €1,300/mo · H100 €1,590/mo
Pros
- Hourly or monthly billing
- GDPR-friendly EU hosting
- Big-VRAM options
Cons
- Popular GPUs sell out
- Setup slower than cloud pods
Servers can be delivered with PyTorch/CUDA images pre-installed.
5. Vast.ai
- GPU marketplace, 68+ GPU types
- On-demand, interruptible and reserved pricing
- Docker templates for AI tools
- Market pricing: RTX 4090 from ~$0.30/hr · A100 80GB from ~$0.43/hr · interruptible 50%+ cheaper
Pros
- Often the lowest prices
- Per-second billing
- Great for experiments
Cons
- Host quality varies
- Not for strict compliance
Choose from official NVIDIA CUDA Docker images or PyTorch templates on any marketplace GPU.
How to Set Up CUDA on a GPU VPS
On Ubuntu 24.04 with an NVIDIA GPU, install the driver and CUDA toolkit from NVIDIA’s repository:
sudo apt update && sudo apt install -y ubuntu-drivers-common
sudo ubuntu-drivers install # installs the recommended NVIDIA driver
sudo reboot
nvidia-smi # check the GPU and driver
# CUDA toolkit (from NVIDIA's apt repo)
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64/cuda-keyring_1.1-1_all.deb
sudo dpkg -i cuda-keyring_1.1-1_all.deb
sudo apt update && sudo apt install -y cuda-toolkit
nvcc --versionFor most AI work you do not even need the full toolkit: pip install torch ships its own CUDA runtime. Just make sure the NVIDIA driver is recent enough.
FAQ
A virtual private server with an NVIDIA GPU that supports CUDA, NVIDIA’s platform for GPU computing.
No. CUDA is NVIDIA-only. AMD GPUs use ROCm instead, which PyTorch also supports.
A 16–24 GB card such as the RTX A4000, RTX A5000 or RTX 4090 is ideal for development and AI experiments.
Conclusion
For an always-on CUDA VPS, GPU Mart offers the best prices. For hourly CUDA work, RunPod and Lambda are the easiest to start with.
Prices were checked in September 2026 and change often. Always confirm current pricing on the provider’s website before you order.