Frequently Asked Questions

TurboGPU is a cloud GPU rental service that gives you a full Linux GPU instance (Ubuntu 22.04 + CUDA) with a dedicated NVIDIA GPU. You connect from any device over SSH or a browser-based Jupyter studio, and pay only for the time you use. There is no hardware to buy, maintain, or upgrade.

Managed notebook services limit you to their runtimes and time out your sessions. TurboGPU gives you a complete Linux machine with full root access. You can install any software, run AI and ML workloads, render, compile, or do anything you would on a physical GPU box.

You need a device with an internet connection (Windows, Mac, Linux, iPad, or Android), an SSH client or terminal (built into Mac and Linux, free on Windows), and a TurboGPU account with at least $1 in credit. Prefer a GUI? Every instance also ships a browser-based Jupyter studio.

TurboGPU instances are available in multiple data center regions across North America and Europe. During deployment, you can select the region closest to you for the lowest latency. We are actively expanding to additional regions.

TurboGPU uses a pay-as-you-go credit system. Credits are denominated in US dollars — 1 credit = $1. You add credits starting from $1, then choose a GPU plan. Billing is per-second while your machine is running. When you stop your machine, billing stops instantly. There are no subscriptions, contracts, or hidden fees.

You are charged per second only while your machine is in the Running state. Stopped machines do not incur charges. Credits are deducted from your balance in real time.

We accept Visa and Mastercard credit and debit cards through our secure payment processor. All transactions are encrypted and PCI-compliant.

Unused credits can be refunded within 14 days of purchase. Contact support@turbogpu.tech for refund requests.

If your credit balance reaches zero while a machine is running, the machine will be stopped automatically. Your files and machine state are preserved for 24 hours.

We offer four tiers: Starter (RTX A4000, 16 GB), Standard (RTX 4090, 24 GB), Pro (L40S, 48 GB), and Power (A100 80GB). All plans include NVMe storage, multiple vCPUs, and full root access.

Most machines are ready in under 60 seconds. Once running, you receive your SSH connection details immediately in your dashboard.

Yes. When you stop a machine, your files, installed packages, and disk state are preserved for 24 hours.

Yes. Work headless over SSH, run notebooks in the browser-based Jupyter studio, or launch lightweight GUI apps through the noVNC browser desktop.

All TurboGPU instances run Ubuntu 22.04 with full root access. NVIDIA drivers, CUDA, cuDNN, and PyTorch are pre-installed.

Copy the SSH command from your dashboard, or open the browser-based Jupyter studio. For coding, the VS Code Remote-SSH extension works great.

SSH needs very little — 5 Mbps is plenty. A graphical noVNC session benefits from 25 Mbps or more.

Select the data center region closest to your physical location and use a wired Ethernet connection where possible.

Yes. Forward any port over SSH (for example -L 8888:localhost:8888) to reach Jupyter, a web UI, or a dev server as if it were running locally.

Yes. Use any SSH client app, or open the Jupyter studio directly in your mobile browser.

Anything CUDA-accelerated: PyTorch and TensorFlow training, Stable Diffusion, LLM inference, Blender/OptiX rendering, video encoding, scientific computing, and more.

Yes. Docker and the NVIDIA Container Toolkit are available, so you can run any GPU-enabled container image.

Absolutely. TurboGPU is used for AI and machine learning, 3D rendering, data science, software builds, CAD, and more.

No. Crypto mining and blockchain proof-of-work computation are prohibited under our Terms of Service.

Check that you have sufficient credit. Verify region availability by trying a different region. If the issue persists, check the status page or contact support@turbogpu.tech.

Make sure your machine is Running. Double-check the host, port, and key or password shown in your dashboard. Ensure your local firewall is not blocking outbound SSH (port 22).

Prefer SSH or the Jupyter studio for most work. If you need the noVNC desktop, switch to a wired connection, lower the resolution, and connect to the nearest region.

Run nvidia-smi to confirm the GPU is visible. If the drivers misbehave, stop and redeploy the instance. You have full root to reinstall drivers if needed.