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Enterprise-Grade Nvidia GPU Servers for Demanding Workloads
| Nvidia GPUs | GPU Memory | CPU | Storage | Network | Best For | Order |
|---|---|---|---|---|---|---|
| RTX PRO 6000 (Blackwell) 🔥 | 96GB | 32Cores | 400GB SSD | 1Gbps | Large AI models, LLM inference, High workloads | |
| RTX PRO 5000 (Blackwell) 🔥 | 48GB | 24Cores | 320GB SSD | 500Mbps | AI Inference, simulation, advanced rendering | |
| RTX PRO 4000 (Blackwell) 🔥 | 24GB | 24Cores | 320GB SSD | 500Mbps | Mid-scale AI workloads, 3D rendering, visual computing | |
| RTX PRO 2000 (Blackwell) 🔥 | 16GB | 16Cores | 240GB SSD | 300Mbps | Entry AI development, lightweight rendering, GPU acceleration | |
| RTX 5090 🔥 | 32GB | 32Cores | 400GB SSD | 500Mbps | AI experimentation, creative workloads, GPU compute tasks | |
| RTX 5060 🔥 | 8GB | 16Cores | 240GB SSD | 200Mbps | Light AI inference, development, GPU-accelerated apps | |
| RTX 4090 | 24GB | 36Cores | 240GB SSD + 2TB NVMe + 8TB SATA | 100M-1Gbps Shared, Upgradable | Deep learning, Stable Diffusion, GPU rendering | |
| NVIDIA H100 | 80GB HBM | 36Cores | 240GB SSD + 2TB NVMe + 8TB SATA | 1Gbps | Large-scale AI training, transformer models, HPC | |
| NVIDIA A100 | 80GB HBM | 36Cores | 240GB SSD + 2TB NVMe + 8TB SATA | 1Gbps | AI training, inference clusters, data analytics | |
| RTX A6000 | 48GB | 36Cores | 240GB SSD + 2TB NVMe + 8TB SATA | 1Gbps | Rendering, simulation, AI model training | |
| RTX A5000 | 24GB | 24Cores | 240GB SSD + 2TB SSD | 100M-1Gbps Shared, Upgradable | AI development, content creation, visualization | |
| RTX A4000 | 16GB | 24Cores | 320GB SSD | 500Mbps | Lightweight AI tasks, CAD, GPU rendering | |
| RTX A40 | 48GB | 36Cores | 240GB SSD + 2TB NVMe + 8TB SATA | 100M-1Gbps Shared, Upgradable | Virtual workstations, rendering farms, AI inference |
Keys Features of GPU Hosting & Rental
Dedicated Server Environment
✨ Optimized for Long-Running Tasks
Data Center Infrastructure
Blackwell Architecture
✨ Enterprise Hardware Foundation
24/7 Technical Support
Proactive Infrastructure Monitoring
✨ Operational Reliability
Low-Latency Remote Access
Public IP & Full Port Control
✨ Network & Access
Full Root / Administrator Access
Persistent Storage Environment
✨ System Flexibility
No Preemption or Forced Reclaims
Ideal for Continuous AI Training
✨ Built for Long-Term Workloads
What Can You Do with GPU Servers?
Machine Learning and AI
Scientific Simulations
Video Rendering and Transcoding
Gaming and Virtual Reality
OBS Streaming
Android Emulators
What People Say About DatabaseMart GPU Hosting Solutions
User Case Studies
Background
Ellery is an AI researcher working on training deep learning models for image classification tasks. She requires high-performance computing resources with GPU capabilities to train and optimize her AI models.
Challenges
Need for powerful servers with multiple GPUs to accelerate the training process and handle large datasets.
Requirement for reliable and responsive technical support to troubleshoot issues and optimize model training algorithms.
Desire for cost-effective hosting solutions to minimize expenses while maximizing computing power.
Solution
Ellery chose GPU Mart for its dedicated GPU servers optimized for AI model training. With GPU Mart's state-of-the-art hardware and expert technical support, she could efficiently train and optimize her deep learning models for image classification tasks. The cost-effective pricing of GPU Mart's hosting solutions allowed her to maximize her computing budget without compromising performance.
Outcome
Ellery's AI research projects progressed rapidly with GPU Mart's high-performance servers. She achieved significant improvements in model accuracy and training efficiency, thanks to GPU Mart's reliable infrastructure and responsive technical support. With GPU Mart's scalable hosting solutions, Ellery could easily adjust server resources to meet changing research requirements and accommodate growing datasets.
FAQs of GPU Dedicated Server Hosting
What is hosting with GPU?
What is GPU Dedicated Server?
Compared with CPU-based servers, GPU servers are usually much faster for tasks that can be processed in parallel across multiple cores. This is because GPUs have much more cores than CPUs, which makes them very suitable for decomposing into many small computing tasks that can be executed at the same time. GPU dedicated servers can be purchased or leased from various hosting providers. They differ in GPU type, GPU quantity, available memory and storage. These servers can be managed remotely, allowing users to access their computing resources from anywhere through Internet connections.
What is dedicated server with GPU Rental?
Unlike virtualized GPU instances, dedicated servers with GPU rental offer users full access to the physical hardware, providing more control and customization options. Users can install their own software and configure the server to meet their specific requirements. Additionally, dedicated servers with GPU rental can offer more consistent performance and higher throughput compared to shared resources. When renting a dedicated server with GPU, users can typically choose from a range of specifications, including the type and number of GPUs, the amount of memory and storage, and the processing power of the CPU. The cost of the service will depend on the specifications selected and the duration of the rental.
How do I choose the right GPU instance for my needs?
Can I use a GPU dedicated server for streaming or running Android emulators?
What are some common GPUs used in hosting environments?
Why put a GPU in a server?
Compared to traditional central processing units (CPUs), GPUs are more efficient at handling compute-intensive workloads because they can perform multiple calculations simultaneously. This is due to the fact that GPUs have many more cores than CPUs, which allows them to process data much faster and in parallel.
By adding a GPU to a server, you can accelerate compute-intensive applications and reduce processing times, leading to improved performance, reduced latency, and increased efficiency. This is particularly important for applications that require real-time processing, such as video rendering, transcoding, and streaming, or for tasks that involve large datasets, such as machine learning and data analytics.
How to get a free trial for GPU server?
1. Choose a plan and click 'Order Now'.
2. Enter ‘24-hour free trial’ in the notes section and click “Check Out”.
3. Click 'Submit Trial Request' at the top right corner, and complete your personal information as instructed; no payment is required.
Once we receive your trial request, we’ll send you the login details within 30 minutes to 2 hours. If your request cannot be approved, you will be notified via email.