GPU服务器价格是多少？不同显卡型号怎么收费？
GPU服务器价格因显卡型号、数量和机房位置不同而有差异。RTX 4090单卡方案¥3999/月起，A100单卡方案¥4999/月起。所有价格透明标注在配置页面，包含硬件、带宽和GPU费用，无隐藏附加费。支持灵活月付无长期合约。
Global GPU Dedicated Servers GPU Servers
Global GPU dedicated server rental, NVIDIA A100/RTX 4090 professional GPUs, up to 8 GPUs per server, from ¥3999/mo. 100% bare-metal hardware, no ICP filing, flexible monthly billing. Ideal for AI training, deep learning inference, GPU rendering, and scientific computing. 24/7 support.
1Regions
3Customization
$571 From/mo
99.9%SLA Guarantee
What Are GPU Dedicated Servers?
GPU servers are dedicated physical servers equipped with NVIDIA professional graphics cards, designed for parallel computing intensive workloads. Compared to CPUs, GPUs feature thousands of CUDA cores that significantly accelerate AI model training, deep learning inference, and graphics rendering. TooServer bare-metal GPU servers are deployed across global data centers, offering NVIDIA A100, RTX 4090, and other GPU models with single to 8-GPU multi-card configurations connected via high-speed NVLink interconnect. Unlike GPU cloud servers, each GPU dedicated server provides 100% physically dedicated CPU, memory, and GPU resources with zero overselling, ensuring training tasks are never disrupted by other tenants. Overseas nodes require no ICP filing, with flexible monthly billing and no long-term contracts.
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GPU Server Use Cases
AI Training ServerNVIDIA A100/RTX 4090 accelerating PyTorch, TensorFlow and other deep learning frameworks, multi-GPU NVLink parallel for faster training
GPU Rendering Server4K/8K real-time video rendering and transcoding, large VRAM for complex scenes, no-filing overseas GPU servers for global content delivery
Scientific ComputingThousands of CUDA cores for large-scale parallel computing, ideal for molecular simulation, financial modeling, and weather prediction
AI Inference & 3D ModelingLow-latency AI inference server for real-time response, complex 3D model processing, bare-metal GPU ensures no resource contention
How to Choose a GPU Server?
1
Choose NVIDIA GPU Model
Select the right GPU server for your AI workload RTX 4090 · AI inference and small-scale training A100 · Large-scale AI model training Hot Multi-GPU cluster · Distributed computing
2
Determine GPU Count
Multi-GPU servers with NVLink parallel acceleration Single GPU · Entry-level training and inference 2-4 GPUs · Medium deep learning projects Hot 8 GPUs · Large-scale training and scientific computing
3
Match Supporting Resources
Bare-metal GPU server with 100% dedicated CPU and memory AI Training · Large memory + NVMe storage AI Inference · Balanced low-latency config Hot GPU Rendering · High-freq CPU + large storage · Monthly billing
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CPU2xE5-2698v3 32 Cores 64 Threads
RAM64GB
Storage800G SSD
Bandwidth20M
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CPU2xEPYC-7502 64 Cores 128 Threads
RAM128GB
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Deploy NowGPU Servers Rental FAQ
GPU服务器支持免备案吗？开通后多久能用？
所有海外机房GPU服务器均免备案，付款确认后即开始部署。现货配置最快数小时内完成硬件上架、系统安装及GPU驱动配置，预装CUDA和常用深度学习框架，交付后即可开始训练任务。
What GPU models are available for GPU servers?
Multiple GPU options: Consumer (RTX 4090 24GB, RTX 4080 16GB, RTX 3090 24GB); Professional (A100 40GB/80GB, V100 32GB, A40 48GB, T4 16GB); Entry-level (RTX 3060 12GB). Different models suit different scenarios - contact sales for current availability.
What can GPU servers do? What uses are prohibited?
Suitable for: AI training/inference, deep learning, cloud gaming, 3D rendering, video transcoding, scientific computing. PROHIBITED: Cryptocurrency mining is strictly forbidden - violations result in immediate termination without refund. Other illegal uses also prohibited.
How is the computing performance of GPU servers?
Performance specs (RTX 4090 example): FP32 compute 82.6 TFLOPS; Memory bandwidth 1008 GB/s; 16384 CUDA cores; 128 ray tracing cores; 512 Tensor cores. Real performance: BERT training 15x faster, Stable Diffusion 3 seconds per image.
How to transfer large-scale training data to the server?
Data transfer solutions: 1) Network: FTP/SFTP/rsync for small-medium data; 2) Object storage: S3 compatible for cloud data; 3) High-speed: Aspera tools (paid), 10x faster; 4) Physical: Hard drive shipping for TB-scale datasets; 5) Internal: Free high-speed transfer between servers in same data center.