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GPU Servers

Designed for applications such as graphics rendering, video processing, GPU-accelerated computing, AI training, and inference, GPU Servers provide dedicated GPU resources and physical server capabilities, delivering stable computing power for graphics-intensive and accelerated workloads.

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Recommended Plans

ProductGPUMemoryStorageBandwidthIPPriceAction
E5 - 2620GPU 75032GB RAM1T HDD100M / Unlimited1IP
$169.00/mo$169
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E5 - 2620*2GPU 105032GB RAM1T SSD100M / Unlimited1IP
$219.00/mo$219
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E5 - 2680*2GPU 108032GB RAM1T SSD100M / Unlimited1IP
$658.00/mo$658
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E5 - 2683v4*2GPU AMD WX 710064GB RAM1T SSD100M / Unlimited1 IP
$479.00/mo$479
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Use Cases

Designed for applications requiring GPU acceleration and dedicated physical resources, including graphics rendering, video processing, parallel computing, AI training, and inference workloads.

3D Rendering and Graphic Design

Suitable for 3D modeling, graphics rendering, and visualization workloads, improving rendering and graphics processing efficiency through GPU acceleration.

Video and Image Processing

Suitable for video processing, image processing, and digital content creation workloads, leveraging GPU parallel processing capabilities to accelerate related tasks.

GPU Parallel Computing

Suitable for applications, development testing, and computing workloads that support GPU parallel processing, improving computational efficiency through multi-core GPU acceleration.

AI Training and Inference

Suitable for training small- to medium-sized AI models, inference, and development testing scenarios. Users can select appropriate GPU configurations based on model size and GPU memory requirements.

FAQs

Learn more about GPU Servers and find answers to your questions.

Q1: What Are GPU Servers? What Is the Difference Between GPU Servers and Standard Servers?

A: GPU Servers are high-performance computing servers equipped with Graphics Processing Units (GPUs). They use the powerful parallel computing capabilities of GPUs to accelerate compute-intensive workloads such as AI training, deep learning, scientific computing, and image processing.

Compared with traditional CPU servers, CPUs are designed for general-purpose computing tasks, while GPUs contain a large number of parallel processing cores that can handle massive amounts of data simultaneously. This makes GPUs more efficient for AI model training, large-scale data analysis, and machine learning inference.

GPU Servers typically provide dedicated physical hardware resources, delivering stable computing performance for enterprises and development teams requiring high-performance computing capabilities.

Q2: What Is the Difference Between GPU Servers and Standard Cloud Servers?

A: Standard cloud servers are typically designed for general-purpose computing workloads and provide resources through virtualization, making them suitable for websites, applications, and other common workloads.

GPU Servers are designed for high-performance computing scenarios and are equipped with dedicated GPU hardware resources, providing stronger parallel processing capabilities for AI training, model inference, and graphics computing tasks.

For workloads requiring continuous GPU computing power, dedicated GPU Servers can provide more stable and reliable performance.

Q3: What Applications Are Suitable for GPU Servers?

A: GPU Servers are mainly designed for workloads requiring significant parallel computing capabilities, including AI model training, deep learning, machine learning, image recognition, natural language processing, scientific computing, video analysis, and large-scale model inference.

Compared with traditional servers relying mainly on CPUs, GPU Servers accelerate computing tasks through GPU processing capabilities, significantly improving the efficiency of complex algorithms and large-scale data processing.

For AI companies, research institutions, development teams, and platforms deploying intelligent applications, GPU Servers provide more efficient computing support.

Q4: Are GPU Servers Suitable for Deploying Private AI Services?

A: Yes. Deploying private AI services typically requires a stable, secure, and controllable computing environment.

GPU Servers provide dedicated computing resources, allowing enterprises to deploy model training, inference services, and AI application platforms based on their requirements. They are suitable for scenarios with high demands for data security and computing performance.

Q5: How to Choose the Right GPU Server Configuration?

A: When selecting a GPU Server configuration, users should consider application type, model size, computing requirements, and budget.

For model training workloads, important factors include GPU model, GPU memory capacity, GPU quantity, and computing performance. For AI inference workloads, users should consider concurrent requests, response time, and operational stability.

Different AI applications have different GPU resource requirements, so configurations should be selected based on actual usage scenarios.

Not Sure Which GPU Server Is Right for You?

Choose the right GPU Server configuration based on your workloads and computing requirements, or contact our support team for professional recommendations.

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