GPU Server Questions

Cloud Clusters offers a wide range of GPU‑based servers tailored to different use cases. Here’s a breakdown to help you decide:

GeForce RTX series (e.g. RTX 4060, RTX 3060 Ti, RTX 4090): Ideal for gaming, live‑streaming, 3D rendering, and entry‑level AI tasks. For example, the RTX 4060 server offers ~15 TFLOPS of FP32 performance and 8 GB VRAM — great for image generation or game testing .

Quadro series (e.g. P600, T1000, A4000, A5000): Built for professional workloads — CAD/CGI, HD video editing, Android emulation, and mid‑level machine learning. These Quadro GPUs typically include ECC memory and longer support life

Data‑center/Tesla series (e.g. Tesla K80, V100; A40, A100, H100): Designed for AI training, large machine learning models, and high‑performance compute — they offer massive CUDA/Tensor core counts and VRAM (e.g. A100: 40 GB, 19.5 TFLOPS)

The A40 model is perfect for visual computing and deep learning, with 48 GB VRAM and 37 TFLOPS FP32

Thank you for your interest, but we do not accept third-party GPU providers at this time. Our platform offers GPU servers directly to customers and does not support external GPU resource hosting.

Our GPU resources are not shared. Each server and VPS comes with a dedicated GPU card, ensuring that you have access to the full power and resources of the GPU without any sharing or contention. This setup allows you to maximize the performance of your applications and tasks that require GPU acceleration.

Yes. Cloud Clusters offers vLLM GPU clusters with Kubernetes. You can purchase multiple GPU servers, and we will install Kubernetes and configure them into a single cluster.

Here’s a simple breakdown of the differences between our GPU VPS and GPU Dedicated Server, tailored to your needs:

GPU VPS:

Shared Resources: The physical GPU is shared with other users (via virtualization), so you get a portion of its power.
Cost: More affordable, great for lighter tasks (e.g., small-scale AI testing, basic graphics work).

GPU Dedicated Server:
Exclusive Use: You get the entire physical GPU (and all its power) to yourself—no sharing.
Performance: Ideal for heavy workloads (e.g., large AI training, high-end rendering) where speed and consistency matter.
Cost: Higher upfront, but worth it for long-term, resource-heavy needs.

GPU servers, unlike regular servers, are equipped with Graphics Processing Units (GPUs) in addition to Central Processing Units (CPUs). This allows GPU servers to leverage parallel processing power for tasks such as machine learning, data analytics, and scientific simulations, resulting in significantly faster computation times for these workloads. Regular servers rely solely on CPUs for processing, which may not offer the same level of performance for compute-intensive tasks that benefit from GPU acceleration.

Yes. Our GPU servers come with native GPU passthrough by default, allowing your virtual environment to access the physical GPU directly. This ensures near-native GPU performance without any overhead from virtualization.
GPU passthrough is especially important for workloads such as deep learning training (e.g., PyTorch, TensorFlow), high-performance rendering, and CUDA-based applications.

GPU virtualization depends on the GPU model and server architecture:
Some professional GPUs, such as NVIDIA A-series, Quadro, and Tesla cards, support vGPU or SR-IOV virtualization.
Gaming-grade GPUs, such as the RTX 40 series, typically do not support official vGPU, but they can be dedicated to a single VM via GPU passthrough.
If you require vGPU functionality, please contact our technical team for recommendations on compatible GPU servers (e.g., A5000, A40).

Yes. Our GPU servers support CUDA and OpenGL. By default, we pre-install the appropriate driver based on the GPU model you select. If you require a specific driver version to match your environment (such as a particular CUDA or OpenGL version), you may uninstall and install the version you need, or contact our technical team and we can pre-install it for you.

Yes. We offer servers with 2–4 GPUs, and depending on the GPU model, NVLink interconnects are available (e.g., A100, A5000, A6000). This is ideal for large model training, multi-GPU parallel computing, high-bandwidth workloads, and rendering farm applications.

Our GPU servers support all major AI and deep learning frameworks, including PyTorch, TensorFlow, JAX, and MXNet, as well as popular models such as Stable Diffusion, LLaMA, RAG, and video generation models. You can install these frameworks yourself or contact our technical team for assistance with a fully pre-installed environment.

Yes. You can upgrade from a GPU plan to a multi-GPU plan (e.g., 2, 3, or 4 cards) or replace your current GPU with a higher-performance model (e.g., from RTX to A-series, or from A-series to H-series). Availability depends on the server model. Please contact our Sales team for assistance.

Yes. Our GPU servers can install and run Docker, and can be used with the NVIDIA Container Toolkit for GPU-accelerated containers, making it easy to deploy AI training, inference, or other containerized applications.

GPU servers that support CUDA 12.2 typically include models equipped with NVIDIA GPUs from the Ampere architecture or later. This encompasses various RTX series GPUs, such as RTX 3060 Ti, RTX A4000, RTX A5000, RTX A6000, RTX 4090, RTX 4060, and high-end models like the A100. These GPUs are designed to be compatible with CUDA 12.2 and offer improved performance and features for GPU-accelerated computing tasks.

To RDP/access a GPU Windows server, utilize Remote Desktop Client software such as Microsoft Remote Desktop or any compatible RDP client. Enter the server's IP address or hostname, along with your login credentials. For further guidance, visit: How to Access GPU Server.

We do not provide IPMI by default for GPU VPS and GPU dedicated servers. You can manage your GPU servers using your control panel. However, if you do require IPMI for GPU dedicated servers, please contact us for more information.

NVIDIA offers virtual GPU (vGPU) solutions that enable virtualization of GPUs for virtual desktop infrastructure (VDI) environments. These solutions allow multiple virtual machines (VMs) to share a physical GPU or allocate multiple GPUs to a single VM, providing enhanced graphics and performance for demanding workloads in a virtualized and cloud environment.

Here is a list of specific NVIDIA GPU models that support virtualization, as mentioned in the search results:

1. NVIDIA A100 PCIe 40GB
2. NVIDIA A40
3. NVIDIA RTX A6000
4. NVIDIA RTX A5000
5. Tesla V100

Please note that GPU support for virtualization may also depend on the hypervisor software being used. It is recommended to refer to the product support matrix for the specific vGPU software release for detailed information on GPU compatibility.

Currently, we do not support adding extra GPU cards to an existing GPU server.If you require multiple GPUs, we recommend choosing one of our multi-GPU server plan, which are preconfigured to support multiple GPU cards.

Our GPU resources are not shared. Each server and VPS comes with a dedicated GPU card, ensuring that you have access to the full power and resources of the GPU without any sharing or contention. This setup allows you to maximize the performance of your applications and tasks that require GPU acceleration.

GPU servers, unlike regular servers, are equipped with Graphics Processing Units (GPUs) in addition to Central Processing Units (CPUs). This allows GPU servers to leverage parallel processing power for tasks such as machine learning, data analytics, and scientific simulations, resulting in significantly faster computation times for these workloads. Regular servers rely solely on CPUs for processing, which may not offer the same level of performance for compute-intensive tasks that benefit from GPU acceleration.

Cases that typically require upgrading to a GPU server include handling large-scale datasets for machine learning or deep learning tasks, performing complex graphics processing, rendering, streaming, video editing, or accelerating other compute-intensive workloads that require parallel processing, such as multiple Android Emulators.

While both offer dedicated GPU resources, GPU VPS shares CPU and disk resources among users through virtualization, allowing for flexibility and scalability but potentially compromising performance due to resource sharing. Conversely, GPU dedicated servers grant exclusive access to hardware resources, ensuring consistent and dedicated performance for demanding tasks, albeit usually at a higher cost.

Yes, virtualization comes pre-enabled by default on our GPU VPS once it's deployed.

A GPU Dedicated Server is a physical server equipped with one or more high-performance GPUs, fully dedicated to you. It provides maximum computing power for tasks such as AI training, machine learning, 3D rendering, video processing, and other GPU-intensive workloads.

Yes, we do offer GPU servers specifically designed for Android emulators. You can find more details and suitable plans here: https://www.cloudclusters.io/emulator

Yes, we do offer GPU servers specifically designed for Android emulators. You can find more details and suitable plans here: https://www.cloudclusters.io/emulator

Minimum GPU Requirements for Revit software:Graphics Card: Supports DirectX 11, Shader Model 5, with at least 4GB VRAM. Example Entry-Level GPUs: We recommend NVIDIA GTX 1650 for this purpose.

Our GPU dedicated servers can be used to run live streaming projects.Below are our recommended servers:
https://www.cloudclusters.io/streaming
Please share with us your budget and configuration requirements.

Yes, GPU servers can indeed be used to deploy websites, the same as traditional CPU-based servers. However, it's important to note that GPU servers are typically overkill for standard website hosting needs and are more commonly employed for tasks requiring intensive parallel processing, such as machine learning or scientific computing.

Light games and heavy games are terms used to describe the performance requirements and resource usage of different types of video games. The classification of a game as light or heavy depends on factors such as the game's graphics, processing power, memory usage, and overall system requirements.

Light Games:
Light games are typically characterized by their low system requirements and minimal resource usage. These games are designed to run smoothly on a wide range of devices, including low-end or older hardware. Light games often have simple graphics, basic gameplay mechanics, and smaller file sizes. They are suitable for devices with limited processing power, such as smartphones, tablets, and low-spec computers. Examples of light games include puzzle games, casual games, and 2D platformers.

Heavy Games:
Heavy games, on the other hand, are more demanding in terms of system requirements and resource usage. These games often feature high-quality graphics, complex gameplay mechanics, and immersive environments. Heavy games require powerful hardware, including high-end processors, dedicated graphics cards, and ample memory. They may also have larger file sizes due to the high-resolution textures, detailed models, and advanced visual effects. Heavy games are typically found in genres such as first-person shooters, open-world RPGs, and realistic simulations.

The distinction between light games and heavy games is important for gamers and developers alike. Gamers with low-end devices can focus on light games that their systems can handle without performance issues. Developers can optimize their games for different hardware configurations and target specific audiences based on the game's classification as light or heavy.

Here is a list of common light games and heavy games that can be run on Android emulators like BlueStacks or LDPlayer.

Light Games:
1. Candy Crush Saga
2. Subway Surfers
3. Temple Run
4. Clash Royale
5. Angry Birds
6. Fruit Ninja
7. Hill Climb Racing
8. Cut the Rope
9. Plants vs. Zombies
10. Words with Friends

Heavy Games:
1. PUBG Mobile
2. Call of Duty: Mobile
3. Fortnite
4. Genshin Impact
5. Mobile Legends: Bang Bang
6. Free Fire
7. Asphalt 9: Legends
8. Shadowgun Legends
9. Black Desert Mobile
10. Epic Seven

Please note that the performance of these games may vary depending on the specifications of your computer and the settings of the emulator.

Yes, GPU servers can indeed be used to deploy websites, the same as traditional CPU-based servers. However, it's important to note that GPU servers are typically overkill for standard website hosting needs and are more commonly employed for tasks requiring intensive parallel processing, such as machine learning or scientific computing.

Sorry about that, the Express GPU VPS does not support to build VMs as it is already being created by virtualization. Please refer to our dedicated server at https://www.cloudclusters.io/server/dedicated

Depending on the number of instances you plan to run, we recommend:
1–4 instances: Lite GPU Dedicated Server - P600
4–7 instances: Express GPU Dedicated Server - P1000
8–10 instances: Basic GPU Dedicated Server - T1000 or higher
These servers are suitable for daily app testing. We suggest trying them first to confirm the performance meets your needs. For more information, please refer to https://www.cloudclusters.io/emulator

Depending on the number of instances you plan to run, we recommend:
1–4 instances: Lite GPU Dedicated Server - P600
4–7 instances: Express GPU Dedicated Server - P100
8+ instances: Basic GPU Dedicated Server – T1000 or higher
For example, if you run 8–10 instances, the Basic GPU Dedicated Server – T1000 provides sufficient CPU cores, GPU memory, and storage for stable multi-instance performance. We also suggest testing the server first to confirm it meets your requirements. For more information, please refer to https://www.gpu-mart.com/android-emulator-hosting

Depending on the number of BlueStacks instances you plan to run, we recommend:
1–3 instances: Basic GPU Dedicated Server – GTX 1650 or lower
4+ instances: Professional GPU Dedicated Server – RTX 2060 or higher
For example, if you run 3–5 instances, the Basic GPU Dedicated Server – GTX 1650 is a solid choice. It provides stable performance and smooth multi-instance operation for medium-to-heavy mobile games like GOT Conquest. Since resource usage may vary depending on accounts and gameplay, we still suggest testing first to ensure it meets your needs.For more information, please refer to https://www.cloudclusters.io/emulator

Running 3 simultaneous OBS instances requires a GPU with solid and stable encoding performance. We recommend the Basic GPU Dedicated Server – GTX 1660 and above plan, which offers reliable NVENC encoding suitable for up to three concurrent streams. You may check the available models and details here: https://www.cloudclusters.io/streaming
Since the actual performance may vary depending on your bitrate and resolution settings, we still recommend starting with a trial to ensure the server meets your needs.

To help you choose the most suitable server, please let us know which game you plan to play. Different games have very different requirements for GPU power, CPU performance, VRAM, and bandwidth, so we recommend based on the specific title. You may also check our Gaming GPU server page here: https://www.cloudclusters.io/gaming.

GTA V is a large 3D game that requires strong GPU performance. To ensure smooth gameplay and stable graphics, we recommend using a GPU dedicated server.Our suggested option is the Advanced GPU Dedicated Server – RTX 3060 Ti, which delivers excellent FPS and visual quality when running GTA V. You may check the available gaming GPU servers here: https://www.cloudclusters.io/gaming
We also recommend testing the server first so you can make sure it meets your expectations for graphics quality and FPS.

To recommend the most suitable rendering server, we first need to know which rendering software you are using (e.g., Blender, Octane, Redshift, V-Ray, etc.).Different render engines have different requirements for GPU architecture, VRAM capacity, CUDA/OptiX support, and overall performance.You may also check our GPU rendering server options here: https://www.cloudclusters.io/rendering

If you are using Blender for GPU rendering (Cycles/OptiX), we recommend at least a server with an NVIDIA GTX 1660. It supports CUDA and OptiX, and is suitable for basic to moderate rendering workloads.
If your scenes are more complex or you need faster rendering performance, you can consider higher-end GPUs. You may check the available rendering server options here: https://www.cloudclusters.io/rendering
We also suggest testing the server first to ensure it meets your performance needs.

For 4K video editing, we recommend the Advanced GPU Dedicated Server – RTX 3060 Ti. It provides stronger decoding and timeline playback performance, making editing smoother. We also suggest testing first to ensure it fits your workflow.For more information, please refer to https://www.cloudclusters.io/server/gpu

To recommend the most suitable GPU server, we usually need the following details:
Streaming software and number of instances (e.g., 3 OBS, 3 Stream)
Resolution (720p, 1080p, 2K, 4K)
Frame rate (30fps, 60fps)
Streaming frequency and duration (how many hours per day? 24/7?)
This information helps us evaluate GPU encoding, VRAM, and bandwidth requirements to suggest the best server configuration. You can also check our GPU streaming server options and try them out: https://www.cloudclusters.io/server/gpu

For continuous 24/7 livestreaming, we recommend a Basic GPU Dedicated Server – GTX 1650 or higher. Such servers offer stable GPU encoding performance and better long-term reliability. If you plan on long-duration streaming, we recommend testing first to ensure server stability and network bandwidth are sufficient. For more information, please refer to https://www.cloudclusters.io/streaming

To recommend the most suitable server, we usually need the following details:
Usage: Training / Inference / RAG / Fine-tuning / API deployment
Platform / Framework: Ollama, Hugging Face, TensorFlow, PyTorch, etc.>br>Model info: model name (e.g., Llama 3.3), model parameters (B), model size (GB)
Concurrency: Number of simultaneous requests/users
Deployment: Single GPU / Multi-GPU / Multi-node
This helps us assess GPU compute, VRAM, and network requirements. You may also check our AI / LLM hosting page: https://www.cloudclusters.io/cloud/vllm

If you only plan to use the server for personal gameplay of Crossfire West, you can choose our “Lite GPU Dedicated Server - P600”. Although the P600 is an entry‑level GPU, Crossfire West has modest GPU requirements and it should handle basic gameplay. If you want higher graphics quality or more stable performance, we recommend selecting a server with a more powerful GPU or a higher‑spec configuration.

We recommend using at least GPU Dedicated Servers – P00 and above. You can also find more details here: https://www.gpu-mart.com/obs-gpu.
Notes:
For 1080P@60fps live streaming, select P600.
For 2K@60fps, select, P1000.
For 4K@60fps, select T1000, GTX1650, GTX1660 and above.

We recommend using at least GPU Dedicated Servers – GTX1650 and above. You can refer to this page for more plan details: https://www.cloudclusters.io/rendering

We recommend using at least a GPU Dedicated Server – RTX2060 and above. These servers provide strong computational and graphics power to ensure the smooth operation of clothing design and simulation software.

We recommend using at least a GPU Dedicated Server – RTX A4000 and above. A minimum of 16GB GPU memory is recommended to ensure efficient speech recognition and model inference.

We recommend using at least a Basic GPU Server – GTX1650 and above. This configuration is suitable for playing Global Mirm and ensures a basic gaming experience.

We recommend using at least a Lite GPU Dedicated Server - P600 and above with Windows OS. This reporting tool is used for data visualization and requires DirectX 11 or higher graphics support to ensure smooth report generation and graphical rendering.

We recommend using GPU Dedicated Servers such as RTX 2060, RTX 3060Ti, RTX A4000, or RTX A5000. These servers provide powerful GPU performance to support high-quality visual effects production and rendering tasks.