What operating systems are supported on GPU Cloud Servers?

Cyfuture Cloud GPU Cloud Servers support both Linux and Windows operating systems, including popular AI/ML‑focused Linux distributions and enterprise‑grade Windows Server editions. Customers can generally choose from standard public OS templates or bring their own licensed images where applicable, as long as they are compatible with the underlying GPU drivers and virtualization stack.​

Typical supported categories include:​

> Linux distributions commonly used for AI/ML, data science, and DevOps workloads (for example, Ubuntu Server, CentOS/AlmaLinux/Rocky‑style enterprise Linux, and similar variants where NVIDIA CUDA and other GPU stacks are supported).​

> Windows Server editions optimized for GPU‑accelerated workloads, remote visualization, and application hosting (for example, modern, supported Windows Server releases comparable to Windows Server 2012 R2 or later in other Cyfuture Cloud offerings).​

In addition, GPU‑optimized images or templates may be available that come pre‑configured with GPU drivers, CUDA, and common AI/ML frameworks, which significantly reduce setup time.​

Supported OS types

Cyfuture Cloud positions GPU servers as flexible compute resources that allow customers considerable freedom of operating system choice. The exact catalog may vary by region, GPU model, and product plan, but it typically covers the following families:​

Linux server distributions that are widely adopted for GPU work, such as:

- General‑purpose distributions (for example, Ubuntu‑like images widely used for AI/ML stacks).​

- Enterprise‑style distributions similar to RHEL derivatives (AlmaLinux, Rocky Linux, etc.) which are often preferred in production environments.​

- Windows GPU servers that ship with pre‑installed Windows Server and full administrative access for running GPU‑accelerated Windows workloads.​

In many cases, these images are provided as ready‑to‑deploy templates in the control panel, ensuring that the base OS is validated to work correctly with virtualized or pass‑through GPUs.​

Recommended OS choices for GPU workloads

While a broad OS spectrum is technically supported, some platforms are better aligned with GPU‑intensive use cases. The following patterns are typically recommended for Cyfuture Cloud GPU environments:​

For AI/ML and data science:

- Linux distributions with strong CUDA, PyTorch, TensorFlow, and driver ecosystem support, such as Ubuntu‑style AI/ML‑ready templates often used across GPU clouds.​

- These images may come preloaded with NVIDIA drivers, CUDA toolkit, and container runtimes to speed up model training and inference pipelines.​

For Windows‑centric or .NET/desktop graphics workloads:

- Windows GPU servers with modern Windows Server releases, RDP access, and GPU driver integration for workloads like rendering, simulation GUIs, or proprietary Windows applications.​

Choosing an OS that is already optimized for GPU drivers and toolchains usually reduces configuration overhead, accelerates time‑to‑value, and minimizes compatibility issues.​

Key considerations when selecting an OS

Selecting the right operating system on Cyfuture Cloud GPU Servers depends on workload type, software stack, and licensing needs. Some important aspects to consider are:​

Driver and framework support:

- Ensure that your chosen Linux or Windows version is supported by the GPU vendor’s drivers (for example, NVIDIA data center drivers and CUDA versions) and by your AI/ML or rendering frameworks.​

Application ecosystem and tooling:

- Linux is often preferred for open‑source AI/ML stacks, DevOps pipelines, and container‑native architectures, while Windows is more suitable for Microsoft‑centric or legacy desktop applications.​

Management and integration:

- Consider how the OS aligns with your configuration management tools, CI/CD pipelines, monitoring stack, and security baselines already standardized in your organization.​

Verifying compatibility in a smaller test environment before scaling production GPU clusters is generally advisable to avoid runtime surprises.​

Conclusion

Cyfuture Cloud GPU Cloud Servers support a broad range of Linux and Windows operating systems, giving organizations the flexibility to match their GPU infrastructure with their existing tooling and application stack. Linux‑based AI/ML‑ready templates and Windows GPU servers together cover most modern use cases, from deep learning and big data analytics to visualization, simulation, and proprietary Windows workloads.​

By selecting an OS that is validated for GPU drivers and aligned with your workload requirements, you can unlock the full performance of Cyfuture Cloud’s GPU infrastructure while simplifying deployment and ongoing operations.​

Follow‑up questions and answers

1. Can I change the operating system on an existing GPU Cloud Server?

In many cloud environments, changing the OS on a running GPU instance requires rebuilding or redeploying the server from a different template rather than performing an in‑place upgrade. A common pattern is to:​

- Take backups or snapshots of important data.

- Provision a new GPU server with the desired OS template.

- Migrate application data and configuration to the new instance.

Exact options and automation paths can depend on the control panel and image management features exposed for Cyfuture Cloud GPU offerings.​

2. Do I need to install GPU drivers myself?

If you use an AI/ML‑ready or GPU‑optimized template, the GPU drivers, CUDA toolkit, and core libraries are often pre‑installed to speed up onboarding. If you select a generic Linux or Windows image, you may need to manually install and maintain the correct GPU drivers and CUDA/ROCm versions that match your workload and framework requirements.​

3. Is containerized GPU workload (Docker, Kubernetes) supported regardless of OS?

Containerized GPU workloads are typically supported on Linux‑based GPU servers using Docker plus vendor‑provided container runtimes (for example, NVIDIA Container Toolkit) and can integrate with Kubernetes‑style orchestrators. On Windows GPU servers, container options may exist but are often more limited and dependent on specific Windows and driver combinations than on Linux.​

4. Can I bring my own custom or licensed OS image?

Some cloud platforms allow bring‑your‑own‑image (BYOI) or custom image upload, provided the image is compatible with the virtualization environment and GPU passthrough or vGPU stack. When using a custom image with GPU hardware, it is essential to validate that the OS, kernel, and drivers support the GPUs and required APIs such as CUDA or ROCm.​

5. Which OS is best for deep learning on Cyfuture Cloud GPU Servers?

For deep learning, AI/ML‑oriented Linux distributions with ready CUDA, cuDNN, PyTorch, TensorFlow, and similar frameworks are typically the most effective choice. These environments usually offer better community support, container images, and tooling for large‑scale training and inference compared to general‑purpose or non‑GPU‑tuned operating systems.​