GPU Dedicated Servers
Supercharge your AI, machine learning, and high-performance computing workloads with our powerful GPU servers featuring the latest NVIDIA GPUs.
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What are GPU Servers?
GPU servers are specialized computing systems equipped with powerful NVIDIA graphics processing units designed for parallel processing tasks. Unlike traditional CPU-based servers, GPU servers excel at handling thousands of simultaneous operations, making them ideal for AI, machine learning, deep learning, 3D rendering, scientific computing, and data analytics workloads.
Key Features of Our GPU Servers
AI Acceleration
Train deep learning models 10-100x faster than CPU-only servers with CUDA and TensorRT support.
Real-Time Rendering
Render 3D graphics, visual effects, and animations with professional-grade GPU performance.
Parallel Processing
Process massive datasets in parallel for scientific research and data analytics.
CUDA Support
Full NVIDIA CUDA toolkit support for GPU-accelerated application development.
GPU Server Applications & Use Cases
Machine Learning & AI
Train neural networks, develop AI models, and deploy machine learning inference at scale.
Video Production & Encoding
Process 4K/8K video, real-time transcoding, and professional video editing workflows.
3D Rendering & CGI
Render architectural visualizations, product designs, and cinematic visual effects.
Scientific Research
Computational chemistry, molecular dynamics, climate modeling, and genome sequencing.
Financial Modeling
High-frequency trading algorithms, risk analysis, and quantitative financial simulations.
VR/AR Development
Develop and test virtual reality applications with real-time graphics processing.
GPU Server Specifications & Features
Available GPU Models:
- NVIDIA RTX 4090 - Latest generation for AI & rendering
- NVIDIA A100 - Data center GPU for AI training
- NVIDIA V100 - Proven performance for deep learning
- NVIDIA T4 - Efficient inference and edge computing
- Custom configurations available on request
Server Features:
- NVMe SSD storage for fast data access
- High-bandwidth network connectivity
- Pre-installed ML frameworks (TensorFlow, PyTorch)
- CUDA toolkit and cuDNN libraries
- Jupyter Notebook environment
- Docker and Kubernetes support
When Do You Need a GPU Server?
Perfect for GPU Servers:
- Training deep learning models
- 3D rendering and animation
- Video transcoding and processing
- Cryptocurrency mining operations
- Scientific simulations and research
- Computer vision applications
- Natural language processing
Consider CPU Servers Instead:
- Standard web hosting
- Database servers (unless GPU-accelerated)
- Email and file servers
- Traditional business applications
- Simple computational tasks
- General-purpose virtualization
Frequently Asked Questions
What GPU models do you offer?
We offer a range of NVIDIA GPUs including RTX 4090, A100, V100, and T4. The specific models available vary by location. Contact our sales team for current availability and pricing for specific GPU configurations.
Can I use multiple GPUs in one server?
Yes! We offer multi-GPU configurations with 2, 4, or even 8 GPUs in a single server. Multi-GPU setups are ideal for distributed training and scaling deep learning workloads.
Are CUDA and cuDNN pre-installed?
Yes, we can pre-install NVIDIA CUDA toolkit, cuDNN, and popular ML frameworks like TensorFlow and PyTorch. You can also choose to install your preferred software stack manually.
How much faster is GPU training compared to CPU?
GPU acceleration typically provides 10-100x speedup for deep learning tasks compared to CPU-only training. The exact speedup depends on the model architecture, batch size, and data pipeline efficiency.
Can I use GPU servers for rendering?
Absolutely! Our GPU servers are excellent for 3D rendering with software like Blender, V-Ray, Octane Render, and Redshift. GPUs dramatically reduce rendering times compared to CPU-only rendering.
What operating systems support GPU computing?
We support Ubuntu, CentOS, and other Linux distributions optimized for GPU computing. Windows Server is also available. Most deep learning work is done on Linux due to better driver support and framework compatibility.
How much GPU memory do I need?
Memory requirements vary by use case. Small models may work with 8-12GB, while large language models and high-resolution image processing need 24GB or more. We can help you choose the right GPU based on your specific workload.
Do you offer hourly billing for GPU servers?
Yes, we offer both monthly and hourly billing options for GPU servers. Hourly billing is perfect for occasional use, batch processing, or project-based work where you only need GPUs temporarily.
Ready to Accelerate Your Workload?
Get started with GPU-powered computing today. Our team can help you choose the perfect configuration.
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