Training and fine-tuning
Train LLMs or vision models with PyTorch and TensorFlow. Up to 80 GB VRAM on a single GPU.
GPU server rental
Your GPU server, ready in minutes, billed by the hour
You get a complete server with a GPU, not just a card: CPU, memory, disk, Ubuntu and CUDA installed. Pick a configuration from the list, the server starts, and your SSH and Jupyter details appear in the panel. AI agents watch the server health and your spend for you.
- A100, H100, RTX 4090 and more, live stock
- Ubuntu + CUDA ready; root SSH and Jupyter
- Hourly rate; stop the server, stop paying
How it works
Pick, launch, connect
No setup. The server comes with a ready image; you just run your job.
- 01
Pick a configuration
See GPU, memory, CPU and disk on the live list; choose secure or community cloud. Prices are hourly and transparent.
- 02
Launch the server
If your balance covers one hour the server starts right away. Ubuntu, CUDA and drivers come installed; open the ports you need.
- 03
Connect and run
Your root SSH password and Jupyter address are in the panel. Stop, restart or terminate whenever you like.
Use cases
From model training to image generation
The same server for a quick experiment or weeks of training; duration and cost stay in your hands.
Inference and APIs
Run vLLM, Ollama or your own service; open an http port and serve an API.
Image and video generation
A ready environment for Stable Diffusion, ComfyUI and similar tools; work via Jupyter or a web UI.
Data processing and rendering
Finish heavy compute, 3D rendering and analysis on an hourly server, then shut it down.
AI agents
An agent watches your server; you focus on the work
From launch to shutdown an AI agent accompanies every server. It checks health, verifies ports, tracks your spend and warns you before the balance runs out. If it does, the server is stopped without data loss.
Launch and setup
Allocates the server, loads the image, opens the ports and writes the access details to the panel. If the launch fails, nothing is charged.
Health and usage
GPU, memory and reachability are checked every minute. A restart is suggested for an unresponsive server.
Budget and security
Spend is deducted minute by minute; you get a warning when the threshold is crossed. Every server runs isolated and the root password is visible only to you.
Configurations and hourly prices
Last update: Nov 2, 2026 04:18 (Istanbul)
| Launch server | |||||||
|---|---|---|---|---|---|---|---|
| RTX PRO 6000 NVIDIA | 96 GB | 24.04 | 16 vCPU 94 GB RAM | 30 GB | $3.735 / hr≈ $89.64 / dayHigh stock | — | Launch server |
| H200 SXM NVIDIA | 141 GB | 24.04 | 12 vCPU 188 GB RAM | 30 GB | $7.935 / hr≈ $190.44 / dayMedium stock | $5.385 / hr≈ $129.24 / dayLow stock | Launch server |
| H100 SXM NVIDIA | 80 GB | 24.04 | 16 vCPU 188 GB RAM | 30 GB | $5.985 / hr≈ $143.64 / dayMedium stock | — | Launch server |
| PRO 6000 MIG 24GB NVIDIA | 24 GB | 24.04 | 4 vCPU 31 GB RAM | 30 GB | $1.035 / hr≈ $24.84 / dayMedium stock | — | Launch server |
| B200 NVIDIA | 180 GB | 24.04 | 24 vCPU 251 GB RAM | 30 GB | $11.985 / hr≈ $287.64 / dayLow stock | — | Launch server |
| RTX PRO 6000 MaxQ NVIDIA | 96 GB | 24.04 | 32 vCPU 249 GB RAM | 30 GB | — | $2.460 / hr≈ $59.04 / dayLow stock | Launch server |
| RTX PRO 6000 WK NVIDIA | 96 GB | 24.04 | 12 vCPU 125 GB RAM | 30 GB | $3.885 / hr≈ $93.24 / dayLow stock | — | Launch server |
| H100 NVL NVIDIA | 94 GB | 24.04 | 19 vCPU 150 GB RAM | 30 GB | $4.785 / hr≈ $114.84 / dayLow stock | $3.885 / hr≈ $93.24 / dayLow stock | Launch server |
| A100 PCIe NVIDIA | 80 GB | 24.04 | 16 vCPU 117 GB RAM | 30 GB | $2.685 / hr≈ $64.44 / dayLow stock | — | Launch server |
| A100 SXM NVIDIA | 80 GB | 24.04 | 16 vCPU 117 GB RAM | 30 GB | $2.685 / hr≈ $64.44 / dayLow stock | $2.085 / hr≈ $50.04 / dayLow stock | Launch server |
| L40 NVIDIA | 48 GB | 24.04 | 32 vCPU 125 GB RAM | 30 GB | — | $1.035 / hr≈ $24.84 / dayLow stock | Launch server |
| L40S NVIDIA | 48 GB | 24.04 | 24 vCPU 251 GB RAM | 30 GB | — | $1.185 / hr≈ $28.44 / dayLow stock | Launch server |
| PRO 6000 MIG 48GB NVIDIA | 48 GB | 24.04 | 16 vCPU 94 GB RAM | 30 GB | $1.935 / hr≈ $46.44 / dayLow stock | — | Launch server |
| RTX 5090 NVIDIA | 32 GB | 24.04 | 16 vCPU 60 GB RAM | 30 GB | $1.785 / hr≈ $42.84 / dayLow stock | $1.035 / hr≈ $24.84 / dayLow stock | Launch server |
| RTX PRO 4500 NVIDIA | 32 GB | 24.04 | 8 vCPU 62 GB RAM | 30 GB | $1.080 / hr≈ $25.92 / dayLow stock | — | Launch server |
| RTX PRO 4500 SE NVIDIA | 32 GB | 24.04 | 16 vCPU 94 GB RAM | 30 GB | $1.080 / hr≈ $25.92 / dayLow stock | — | Launch server |
| L4 NVIDIA | 24 GB | 24.04 | 16 vCPU 94 GB RAM | 30 GB | $0.885 / hr≈ $21.24 / dayLow stock | — | Launch server |
| RTX 3090 NVIDIA | 24 GB | 24.04 | 32 vCPU 125 GB RAM | 30 GB | $0.750 / hr≈ $18.00 / dayLow stock | — | Launch server |
| RTX 4090 NVIDIA | 24 GB | 24.04 | 8 vCPU 31 GB RAM | 30 GB | $1.335 / hr≈ $32.04 / dayLow stock | — | Launch server |
| RTX PRO 4000 NVIDIA | 24 GB | 24.04 | 12 vCPU 31 GB RAM | 30 GB | $0.855 / hr≈ $20.52 / dayLow stock | — | Launch server |
| RTX 2000 Ada NVIDIA | 16 GB | 24.04 | 6 vCPU 31 GB RAM | 30 GB | $0.360 / hr≈ $8.64 / dayLow stock | — | Launch server |
| RTX A4000 NVIDIA | 16 GB | 24.04 | — | 30 GB | — | $0.255 / hr≈ $6.12 / dayLow stock | Launch server |
| RTX 3070 NVIDIA | 8 GB | 24.04 | 18 vCPU 17 GB RAM | 30 GB | — | $0.195 / hr≈ $4.68 / dayLow stock | Launch server |
No configurations match this search.
Prices are hourly for a server with a single GPU. The daily figure is an estimate for 24 hours of continuous use. Stock and rates change in real time; taxes are not included. The OS column shows the version of the ready Ubuntu image. CPU, memory and disk are the minimums of the machines that GPU is offered on. Secure cloud runs in a data center; community cloud has a lower hourly rate.
GPU server FAQ
How fast does a server start?
Most configurations are running within a minute or two. Once it is up, the panel shows the public IP, SSH port, root password and Jupyter address.
What is the difference between secure cloud and community cloud?
Secure cloud runs on fixed infrastructure in a data center and is recommended for business and long-running jobs. Community cloud has a lower hourly rate and suits trials and short jobs. If no price is shown, that environment has no free server right now.
How do I get started?
Open an account, add balance by bank transfer, pick a configuration and choose “Launch server”. If your balance covers at least one hour the server starts right away; the rate is deducted while it runs.
What happens to my data if I stop the server?
Stopping pauses the hourly charge, keeps the container disk and lets you continue with “Start”. “Terminate” deletes the server and its disk permanently.
What happens when my balance runs out?
The agent warns you when the balance drops below 2 hours. When it is fully used, the server is stopped but the disk is kept. Add balance and start it again.
Launch your GPU server today
Open an account, add balance, pick a configuration. A few minutes later you are connected over SSH.