Nvidia GPU-Powered AI RDP
for AI, Deep Learning
& High-Performance Computing
Powered by high-performance NVIDIA GPUs, AI RDP delivers a low-latency Windows Remote Desktop with GPU experience. Built for AI training, deep learning, model development, and GPU computing, it lets you access powerful cloud computing resources instantly without investing in expensive local hardware.
Why Choose RDP Servers AI RDP?
Built for demanding workloads with reliable performance and easy access.
Pay As You Go
Avoid the high cost of buying a GPU workstation. Choose the right configuration, pay monthly, and get your RDP with GPU server ready quickly.
17+ NVIDIA GPU Models
Choose from 17+ NVIDIA GPU options, from entry-level to high-performance models. Build the right NVIDIA RDP environment for AI workloads.
100% Dedicated Resources
Every GPU RDP includes dedicated GPU, CPU, RAM, and storage. No shared resources or virtualization overhead. Enjoy a stable remote desktop with GPU experience.
AI RDP Plans & Pricing
Choose the right GPU-powered remote desktop plan for your workload.
Advanced GPU Dedicated Server - A4000
- 128GB RAM
- GPU: Nvidia Quadro RTX A4000
- Dual 12-Core E5-2697v2
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 6144
- Tensor Cores: 192
- GPU Memory: 16GB GDDR6
- FP32 Performance: 19.2 TFLOPS
Advanced GPU Dedicated Server - A5000
- 128GB RAM
- GPU: Nvidia Quadro RTX A5000
- Dual 12-Core E5-2697v2
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 8192
- Tensor Cores: 256
- GPU Memory: 24GB GDDR6
- FP32 Performance: 27.8 TFLOPS
Enterprise GPU Dedicated Server - RTX A6000
- 256GB RAM
- GPU: Nvidia Quadro RTX A6000
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 38.71 TFLOPS
Enterprise GPU Dedicated Server - A100
- 256GB RAM
- GPU: Nvidia A100
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 6912
- Tensor Cores: 432
- GPU Memory: 40GB HBM2
- FP32 Performance: 19.5 TFLOPS
Enterprise GPU Dedicated Server - RTX 4090
- 256GB RAM
- GPU: GeForce RTX 4090
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ada Lovelace
- CUDA Cores: 16,384
- Tensor Cores: 512
- GPU Memory: 24 GB GDDR6X
- FP32 Performance: 82.6 TFLOPS
Enterprise GPU Dedicated Server - A40
- 256GB RAM
- GPU: Nvidia A40
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 37.48 TFLOPS
Multi-GPU Dedicated Server- 2xRTX 4090
- 256GB RAM
- GPU: 2 x GeForce RTX 4090
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ada Lovelace
- CUDA Cores: 16,384
- Tensor Cores: 512
- GPU Memory: 24 GB GDDR6X
- FP32 Performance: 82.6 TFLOPS
Multi-GPU Dedicated Server - 3xRTX A5000
- 256GB RAM
- GPU: 3 x Quadro RTX A5000
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 8192
- Tensor Cores: 256
- GPU Memory: 24GB GDDR6
- FP32 Performance: 27.8 TFLOPS
Multi-GPU Dedicated Server - 3xRTX A6000
- 256GB RAM
- GPU: 3 x Quadro RTX A6000
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 38.71 TFLOPS
Multi-GPU Dedicated Server - 4xRTX A6000
- 512GB RAM
- GPU: 4 x Quadro RTX A6000
- Dual 22-Core E5-2699v4
- 240GB SSD + 4TB NVMe + 16TB SATA
- 1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 38.71 TFLOPS
Enterprise GPU Dedicated Server - A100(80GB)
- 256GB RAM
- GPU: Nvidia A100
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 6912
- Tensor Cores: 432
- GPU Memory: 80GB HBM2e
- FP32 Performance: 19.5 TFLOPS
Enterprise GPU Dedicated Server - H100
- 256GB RAM
- GPU: Nvidia H100
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Hopper
- CUDA Cores: 14,592
- Tensor Cores: 456
- GPU Memory: 80GB HBM2e
- FP32 Performance: 183TFLOPS
AI RDP GPU Computing Performance
Compare GPU specifications, Tensor performance, and multi-GPU capabilities for different workloads.
Single GPU Specifications
Complete technical specifications for individual GPU configurations
| GPU | VRAM | Bandwidth | Architecture | CUDA Cores | Tensor Cores | FP16 | BF16 | Compute |
|---|---|---|---|---|---|---|---|---|
| RTX A4000 | 16GB GDDR6 | 448 GB/s | Ampere | 6,144 | 192 (3rd Gen) | ~19.2 TFLOPS* | ~19.2 TFLOPS* | 8.6 |
| RTX A5000 | 24GB GDDR6 | 768 GB/s | Ampere | 8,192 | 256 (3rd Gen) | ~30.8 TFLOPS* | ~30.8 TFLOPS* | 8.6 |
| RTX A6000 | 48GB GDDR6 | 768 GB/s | Ampere | 10,752 | 336 (3rd Gen) | ~38.7 TFLOPS* | ~38.7 TFLOPS* | 8.6 |
| A40 | 48GB GDDR6 | 696 GB/s | Ampere | 10,752 | 336 (3rd Gen) | ~37.4 TFLOPS* | ~37.4 TFLOPS* | 8.6 |
| RTX 4090 | 24GB GDDR6X | 1,008 GB/s | Ada Lovelace | 16,384 | 512 (4th Gen) | 82.6 TFLOPS | 165.2 TFLOPS | 8.9 |
| A100 40GB PCIe | 40GB HBM2 | 1,555 GB/s | Ampere | 6,912 | 432 (3rd Gen) | 312 TFLOPS | 312 TFLOPS | 8.0 |
| A100 80GB PCIe | 80GB HBM2e | 1,935 GB/s | Ampere | 6,912 | 432 (3rd Gen) | 312 TFLOPS | 312 TFLOPS | 8.0 |
| H100 PCIe 80GB | 80GB HBM2e | ~2 TB/s | Hopper | 14,592 | 456 (4th Gen) | 1,513 TFLOPS | 1,513 TFLOPS | 9.0 |
| V100 16GB PCIe | 16GB HBM2 | 900 GB/s | Volta | 5,120 | 640 (2nd Gen) | 125 TFLOPS | N/A | 7.0 |
Multi-GPU Configurations
Combined specifications for multi-GPU setups with interconnect details
| Configuration | Total VRAM | Total Bandwidth | Total FP16 Performance | Tensor Cores | Interconnect | Compute |
|---|---|---|---|---|---|---|
| 2× RTX 4090 | 48GB | 2,016 GB/s | ~1,322 TFLOPS (Sparse) / ~330 TFLOPS (Dense) | 1,024 (4th Gen) | PCIe | 8.9 |
| 3× RTX A5000 | 72GB | 2,304 GB/s | ~750 TFLOPS (Sparse) / ~375 TFLOPS (Dense) | 768 (3rd Gen) | PCIe | 8.6 |
| 3× RTX A6000 | 144GB | 2,304 GB/s | ~929 TFLOPS (Sparse) / ~465 TFLOPS (Dense) | 1,008 (3rd Gen) | PCIe | 8.6 |
| 4× RTX A6000 | 192GB | 3,072 GB/s | ~1,239 TFLOPS (Sparse) / ~620 TFLOPS (Dense) | 1,344 (3rd Gen) | PCIe | 8.6 |
| 4× A100 40GB | 160GB | 6,220 GB/s | 1,248 TFLOPS | 1,728 (3rd Gen) | 6× NVLink | 8.0 |
| 3× V100 16GB | 48GB | 2,700 GB/s | 375 TFLOPS | 1,920 (2nd Gen) | PCIe | 7.0 |
Dense vs. Sparse Performance
Understanding GPU performance metrics for AI workloads
AI GPU RDP Use Case
Match your workload with the right GPU resources for better performance and efficiency.
Single GPU RDP
Individual GPU configurations for focused workloads
Multi-GPU RDP
Multi-GPU configurations for parallel processing and distributed training
Why Choose AI RDP Instead of Buying Your Own Hardware?
Access powerful GPU computing without the cost and complexity of owning physical hardware.
High Upfront Hardware Costs
High-end AI GPU hardware remains expensive. With ongoing demand for AI computing and limited GPU availability, hardware costs and delivery times remain unpredictable. AI RDP eliminates large upfront investments, allowing you to access powerful GPUs on demand without worrying about hardware price fluctuations or supply constraints.
| GPU Model | Launch MSRP | 2021–2023 | 2024 | 2025–Present |
|---|---|---|---|---|
| RTX A4000 | $999 | ~$1,000 | ~$950–1,100 | ~$900–1,000 |
| RTX A5000 | $2,250 | ~$2,250 | ~$2,200–2,500 | ~$2,000–2,400 |
| RTX A6000 | $4,649 | $4,100–7,370 | ~$4,500–6,000 | ~$4,000–5,500 |
| A40 | ~$4,500 | ~$4,500–5,000 | ~$4,200–5,500 | ~$4,000–5,000 |
| RTX 4090 | $1,599 | — | $1,599–2,299 | $1,500–2,000 |
| A100 40GB | ~$10,000 | ~$10,000–12,000 | ~$8,000–12,000 | ~$8,000–10,000 |
| A100 80GB | ~$15,000 | ~$15,000–18,000 | ~$15,000–17,000 | ~$9,500–14,000 |
| H100 80GB | ~$30,000+ | — | $25,000–40,000 | $25,000–30,000 |
High Maintenance and Operating Costs
Owning GPU hardware requires much more than the initial purchase. Additional costs include electricity, cooling, rack space, network bandwidth, hardware maintenance, and system administration.
With AI RDP, maintenance, upgrades, and hardware failures are handled by the provider. Pay only for the resources you need and avoid the ongoing costs of managing physical infrastructure.
Long Deployment Time
Setting up your own AI GPU workstation or server can take weeks or even months. The process involves hardware selection, purchasing, shipping, installation, driver setup, software configuration, and network tuning.
AI RDP provides ready-to-use GPU environments within hours. Pre-configured systems allow developers to start AI development, model training, and testing immediately without complex setup.
Limited Resource Flexibility
Purchased hardware provides fixed computing capacity. When workloads increase, you need to buy additional GPUs. During low-demand periods, expensive hardware may remain underutilized.
With AI RDP, you can scale resources based on your actual needs. Use multiple GPUs for intensive training, switch to lower-cost configurations for inference, and adjust resources between projects without being locked into specific hardware.
FAQs of Nvidia AI RDP Server
Answers to Common Questions About Nvidia GPU-Powered Remote Desktop Servers.
AI RDP is a GPU-powered remote desktop service that provides high-performance NVIDIA GPUs through a Windows Remote Desktop environment. By connecting to a cloud server equipped with dedicated GPU resources, you can use your local device to access powerful computing resources for AI model training, inference, and development.
Simply put, it is like renting a high-performance computer in the cloud. You can operate it through Windows Remote Desktop GPU access just like a local workstation, while getting much more GPU computing power than a typical personal computer.
Yes. AI GPU RDP provides full administrator access, allowing you to install and configure your own software environment. You can install development tools such as Python, Node.js, Rust, Git, Docker, and Conda; AI frameworks including PyTorch, TensorFlow, and MXNet; and development environments such as VS Code, PyCharm, and JupyterLab. You can also upload datasets and model files for your projects. However, the service cannot be used for cryptocurrency mining, illegal activities, or prohibited software.
GPU RDP supports a wide range of AI and machine learning frameworks. Popular options include PyTorch, TensorFlow, Keras, MXNet, and Caffe, along with computer vision libraries such as OpenCV, Pillow, and scikit-image. It also supports AI tools and frameworks including Stable Diffusion, FLUX, Hugging Face Transformers, spaCy, scikit-learn, XGBoost, LightGBM, OpenAI Gym, and Ray RLlib. NVIDIA acceleration libraries such as CUDA, cuDNN, and NCCL are available.
Data security for AI RDP depends on both the service provider and the user. We offer security features such as TLS/SSL encrypted connections, storage protection, server isolation, and firewall protection. Users should also follow security best practices, including using strong passwords, regularly backing up important files, and downloading data before making account or service changes.
After canceling an AI RDP service, the server resources will be released and stored data will be removed according to the provider's data management policy. Before cancellation, make sure to download important files, including training results, model weights, source code, and datasets. Regular backups to local storage or third-party cloud storage are recommended.
Yes. We provide free trial options for AI RDP. New users can register an account and contact our team with their requirements. Based on your workload and configuration needs, we can recommend a suitable NVIDIA RDP trial setup.