Pricing Plans Android Emulator Server
| GPU | RAM memory | CPU | Storage | OS | Monthly | Deploy |
|---|---|---|---|---|---|---|
| GeForce K620 (2 GB GPU Memory) |
16 GB (DDR4 RAM) |
Quad-Core Xeon E3-1270v3 (4 Cores, 8 Threads) |
1TB (120GB + 960GB SSD) |
Windows OS Linux OS |
$99.0 | Order |
| Quadro P620 (2 GB GPU Memory) |
32 GB (DDR4 RAM) |
Xeon E5-2670 (8 Cores, 16 Threads) |
1TB (120GB + 960GB SSD) |
Windows OS Linux OS |
$112.0 | Order |
| GeForce T1000 (8 GB GPU Memory) |
64 GB (DDR4 RAM) |
Eight-Core Xeon E5-2690 (8 Cores, 16 Threads) |
1TB (120GB + 960GB SSD) |
Windows OS Linux OS |
$209.0 | Order |
| GeForce RTX 3060 (8 GB GPU Memory) |
128 GB (DDR4 RAM) |
E5-2697v2 (24 Cores, 48 Threads) |
2TB (240GB + 2TB SSD) |
Windows Server Linux OS |
$445.0 | Order |
Pricing Plans
| GPU | RAM memory | CPU | Storage | OS | Monthly | Deploy |
|---|---|---|---|---|---|---|
| Tesla K80 (24 GB GPU Memory) |
64 GB (DDR4 RAM) |
Xeon E5-2690 (8 Cores, 16 Threads) |
1TB (120GB + 960GB SSD) |
Windows OS Linux OS |
$239.0 | Order |
| Nvidia RTX 4060 (8 GB GPU Memory) |
64 GB (DDR4 RAM) |
Eight-Core E5-2690 (8 Cores, 16 Threads) |
1TB (120GB + 960GB SSD) |
Windows OS Linux OS |
$328.0 | Order |
| Nvidia Quadro RTX A4000 (16 GB GPU Memory) |
128 GB (DDR4 RAM) |
Dual 12-Core E5-2697v2 (24 Cores, 48 Threads) |
240GB SSD + 2TB SSD | Windows OS Linux OS |
$385.0 | Order |
GPU servers, also known as GPU-accelerated servers, are computer servers equipped with powerful Graphics Processing Units (GPUs) in addition to traditional Central Processing Units (CPUs). GPUs are specialized hardware designed to perform complex calculations and render graphics efficiently. While CPUs are more general-purpose processors, GPUs excel in parallel processing tasks, making them highly suitable for computationally intensive workloads.
GPU servers are commonly used in various fields that require significant computational power, such as artificial intelligence (AI), machine learning (ML), deep learning, data science, scientific research, and high-performance computing (HPC). GPUs are particularly effective in tasks involving large-scale data processing, numerical simulations, image and video processing, natural language processing (NLP), and computer vision.
By harnessing the computational capabilities of GPUs, these servers can accelerate the execution of tasks that involve massive amounts of data or require complex calculations. The parallel architecture of GPUs allows for concurrent processing of multiple data streams, resulting in significant speed-ups compared to traditional CPU-based systems. GPU servers are typically employed in data centers or cloud computing environments, providing remote access and scalability for users who require substantial computational resources.
Companies and researchers working on AI and ML applications heavily rely on GPU servers to train and run deep neural networks, which are computationally demanding. GPUs enable faster model training times, leading to quicker development iterations and improved productivity. Additionally, GPUs can handle real-time processing of large datasets, enabling applications like real-time video analytics, autonomous vehicles, and high-frequency trading.
GPU servers come in various configurations, with different numbers and types of GPUs. High-end models may feature multiple GPUs interconnected using technologies like NVIDIA NVLink or AMD Infinity Fabric, enhancing data exchange between GPUs and further boosting performance.
GPU servers are used for a wide range of applications that require significant computational power and parallel processing capabilities. Some common use cases for GPU servers include:
Artificial Intelligence (AI) and Machine Learning (ML): GPU servers are extensively used for training and running deep neural networks. The parallel processing capabilities of GPUs significantly accelerate model training times, enabling faster development iterations and improved AI/ML performance.
Deep Learning: Deep learning, a subset of AI/ML, involves training and running deep neural networks with multiple layers. GPU servers excel at handling the complex calculations and large datasets involved in deep learning tasks, making them essential for various applications like computer vision, natural language processing, speech recognition, and recommendation systems.
Data Science: GPU servers are employed in data science workflows that involve large-scale data processing, data mining, and statistical analysis. GPUs can accelerate tasks like data cleansing, feature extraction, data visualization, and predictive modeling, enabling faster and more efficient data analysis.
Scientific Research: GPU servers are valuable tools for researchers in fields such as physics, chemistry, biology, astronomy, and climate science. These servers can accelerate simulations, data processing, and computational tasks involved in scientific research, allowing researchers to analyze complex data and solve intricate problems more effectively.
High-Performance Computing (HPC): GPU servers play a crucial role in high-performance computing environments. They are used for simulations, numerical analysis, computational fluid dynamics, weather forecasting, molecular modeling, and other computationally intensive tasks. GPUs can significantly enhance the performance and speed of these calculations compared to traditional CPU-based systems.
Video and Image Processing: The parallel processing capabilities of GPUs make them well-suited for real-time video and image processing tasks. GPU servers are used in applications like video transcoding, video analytics, computer vision, virtual reality, augmented reality, and image recognition.
Cryptocurrency Mining: GPUs are commonly employed for cryptocurrency mining, where they perform the complex calculations required to validate and secure transactions on blockchain networks. GPU servers with multiple GPUs can deliver higher mining performance and efficiency. However, we do not allow you to use your GPU to mine cryptocurrencies.
Cloud Computing: GPU servers are widely available in cloud computing platforms, allowing users to access powerful GPU resources on-demand. This is particularly useful for startups, researchers, and organizations that require temporary or scalable access to GPU-accelerated computing without investing in dedicated hardware.
Overall, GPU servers are instrumental in accelerating computationally demanding tasks, enabling faster processing, improved performance, and enhanced productivity in various fields and applications.
To check the GPU on a Windows Server, you can follow these steps:
1. Open the Device Manager: Press the Windows key + X and select “Device Manager” from the menu that appears. Alternatively, you can right-click on the Start button and choose “Device Manager” from there.
2. Expand the Display Adapters category: In the Device Manager window, locate and click on the arrow next to “Display adapters.” This action will expand the category and display the installed GPUs.
3. View the GPU details: Under the Display Adapters category, you will see the name(s) of the installed GPU(s). You can double-click on a specific GPU to view its properties, including the driver version and other information.
4. Use third-party tools: You can also use third-party software to gather more detailed information about your GPU(s). Examples of such tools include GPU-Z, Speccy, and HWiNFO. These tools provide comprehensive information about the GPU, including temperature, clock speeds, memory usage, and more.
Remember that the steps may vary slightly depending on the Windows Server version you are using, but the general process remains the same.
Minecraft servers do not necessarily require a dedicated GPU (Graphics Processing Unit) to function. Minecraft is primarily CPU (Central Processing Unit) intensive and relies heavily on single-threaded performance rather than GPU acceleration.
The server-side of Minecraft primarily handles tasks such as world generation, physics calculations, and multiplayer networking. These tasks are typically handled by the CPU, and the server’s performance is more influenced by the CPU’s clock speed, core count, and efficiency.
However, there are a few scenarios where a dedicated GPU might be beneficial for a Minecraft server:
Rendering Mods or Shaders: If you plan to use rendering mods or shaders that enhance the game’s graphics, a GPU with decent capabilities can help achieve smoother gameplay and better visuals. However, keep in mind that this primarily affects the client-side experience, and the server itself may not necessarily require a GPU for these enhancements.
Dynmap or Web-based Mapping: Some Minecraft servers use plugins like Dynmap to provide web-based maps of the server’s world. These plugins may utilize the GPU to render and display the maps more efficiently, enhancing performance and responsiveness.
Other Game Servers or Applications: If you plan to run other game servers alongside your Minecraft server on the same machine, or if you intend to host other GPU-accelerated applications concurrently, having a GPU can be beneficial for those specific tasks.
In most cases, however, a dedicated GPU is not essential for running a Minecraft server. It is more important to prioritize a high-performance CPU, sufficient RAM, and a stable network connection to ensure smooth gameplay and a responsive server experience.
GPU servers, also known as GPU-accelerated servers, are computer servers equipped with powerful Graphics Processing Units (GPUs) in addition to traditional Central Processing Units (CPUs). GPUs are specialized hardware designed to perform complex calculations and render graphics efficiently. While CPUs are more general-purpose processors, GPUs excel in parallel processing tasks, making them highly suitable for computationally intensive workloads.
GPU servers are commonly used in various fields that require significant computational power, such as artificial intelligence (AI), machine learning (ML), deep learning, data science, scientific research, and high-performance computing (HPC). GPUs are particularly effective in tasks involving large-scale data processing, numerical simulations, image and video processing, natural language processing (NLP), and computer vision.
By harnessing the computational capabilities of GPUs, these servers can accelerate the execution of tasks that involve massive amounts of data or require complex calculations. The parallel architecture of GPUs allows for concurrent processing of multiple data streams, resulting in significant speed-ups compared to traditional CPU-based systems. GPU servers are typically employed in data centers or cloud computing environments, providing remote access and scalability for users who require substantial computational resources.
Companies and researchers working on AI and ML applications heavily rely on GPU servers to train and run deep neural networks, which are computationally demanding. GPUs enable faster model training times, leading to quicker development iterations and improved productivity. Additionally, GPUs can handle real-time processing of large datasets, enabling applications like real-time video analytics, autonomous vehicles, and high-frequency trading.
GPU servers come in various configurations, with different numbers and types of GPUs. High-end models may feature multiple GPUs interconnected using technologies like NVIDIA NVLink or AMD Infinity Fabric, enhancing data exchange between GPUs and further boosting performance.
GPU servers are used for a wide range of applications that require significant computational power and parallel processing capabilities. Some common use cases for GPU servers include:
Artificial Intelligence (AI) and Machine Learning (ML): GPU servers are extensively used for training and running deep neural networks. The parallel processing capabilities of GPUs significantly accelerate model training times, enabling faster development iterations and improved AI/ML performance.
Deep Learning: Deep learning, a subset of AI/ML, involves training and running deep neural networks with multiple layers. GPU servers excel at handling the complex calculations and large datasets involved in deep learning tasks, making them essential for various applications like computer vision, natural language processing, speech recognition, and recommendation systems.
Data Science: GPU servers are employed in data science workflows that involve large-scale data processing, data mining, and statistical analysis. GPUs can accelerate tasks like data cleansing, feature extraction, data visualization, and predictive modeling, enabling faster and more efficient data analysis.
Scientific Research: GPU servers are valuable tools for researchers in fields such as physics, chemistry, biology, astronomy, and climate science. These servers can accelerate simulations, data processing, and computational tasks involved in scientific research, allowing researchers to analyze complex data and solve intricate problems more effectively.
High-Performance Computing (HPC): GPU servers play a crucial role in high-performance computing environments. They are used for simulations, numerical analysis, computational fluid dynamics, weather forecasting, molecular modeling, and other computationally intensive tasks. GPUs can significantly enhance the performance and speed of these calculations compared to traditional CPU-based systems.
Video and Image Processing: The parallel processing capabilities of GPUs make them well-suited for real-time video and image processing tasks. GPU servers are used in applications like video transcoding, video analytics, computer vision, virtual reality, augmented reality, and image recognition.
Cryptocurrency Mining: GPUs are commonly employed for cryptocurrency mining, where they perform the complex calculations required to validate and secure transactions on blockchain networks. GPU servers with multiple GPUs can deliver higher mining performance and efficiency. However, we do not allow you to use your GPU to mine cryptocurrencies.
Cloud Computing: GPU servers are widely available in cloud computing platforms, allowing users to access powerful GPU resources on-demand. This is particularly useful for startups, researchers, and organizations that require temporary or scalable access to GPU-accelerated computing without investing in dedicated hardware.
Overall, GPU servers are instrumental in accelerating computationally demanding tasks, enabling faster processing, improved performance, and enhanced productivity in various fields and applications.
To check the GPU on a Windows Server, you can follow these steps:
1. Open the Device Manager: Press the Windows key + X and select “Device Manager” from the menu that appears. Alternatively, you can right-click on the Start button and choose “Device Manager” from there.
2. Expand the Display Adapters category: In the Device Manager window, locate and click on the arrow next to “Display adapters.” This action will expand the category and display the installed GPUs.
3. View the GPU details: Under the Display Adapters category, you will see the name(s) of the installed GPU(s). You can double-click on a specific GPU to view its properties, including the driver version and other information.
4. Use third-party tools: You can also use third-party software to gather more detailed information about your GPU(s). Examples of such tools include GPU-Z, Speccy, and HWiNFO. These tools provide comprehensive information about the GPU, including temperature, clock speeds, memory usage, and more.
Remember that the steps may vary slightly depending on the Windows Server version you are using, but the general process remains the same.
Minecraft servers do not necessarily require a dedicated GPU (Graphics Processing Unit) to function. Minecraft is primarily CPU (Central Processing Unit) intensive and relies heavily on single-threaded performance rather than GPU acceleration.
The server-side of Minecraft primarily handles tasks such as world generation, physics calculations, and multiplayer networking. These tasks are typically handled by the CPU, and the server’s performance is more influenced by the CPU’s clock speed, core count, and efficiency.
However, there are a few scenarios where a dedicated GPU might be beneficial for a Minecraft server:
Rendering Mods or Shaders: If you plan to use rendering mods or shaders that enhance the game’s graphics, a GPU with decent capabilities can help achieve smoother gameplay and better visuals. However, keep in mind that this primarily affects the client-side experience, and the server itself may not necessarily require a GPU for these enhancements.
Dynmap or Web-based Mapping: Some Minecraft servers use plugins like Dynmap to provide web-based maps of the server’s world. These plugins may utilize the GPU to render and display the maps more efficiently, enhancing performance and responsiveness.
Other Game Servers or Applications: If you plan to run other game servers alongside your Minecraft server on the same machine, or if you intend to host other GPU-accelerated applications concurrently, having a GPU can be beneficial for those specific tasks.
In most cases, however, a dedicated GPU is not essential for running a Minecraft server. It is more important to prioritize a high-performance CPU, sufficient RAM, and a stable network connection to ensure smooth gameplay and a responsive server experience.