Dedicated Servers with GPU: Machine Learning and Professional Rendering
In today’s technology landscape, artificial intelligence (AI), machine learning (ML), and 3D rendering are no longer abstract concepts, but concrete tools driving innovation in every industry. From scientific research to entertainment, from finance to medicine, the ability to process massive amounts of data and create complex simulations has become a crucial competitive advantage. But powering these technologies requires extraordinary computing power—power that only a dedicated server with GPU can provide.
At Servereasy, we’ve seen this need grow and we’ve specialized in delivering custom server solutions equipped with the most powerful NVIDIA GPUs to support the most ambitious projects. In this article, we’ll explore why a dedicated GPU server is the go-to choice for anyone working with AI, ML, and rendering, and how our solutions can accelerate your innovation.
What Is a Dedicated Server with GPU?
A dedicated server with GPU is a physical server whose resources (CPU, RAM, storage) are fully allocated to a single customer, with the addition of one or more Graphics Processing Units (GPUs). Unlike CPUs, designed to handle a wide range of tasks sequentially, GPUs specialize in parallel computing: they can execute thousands of operations simultaneously thanks to their cores (CUDA cores for NVIDIA).
This architecture makes them incredibly efficient for repetitive and mathematically intensive workloads, such as training machine learning models, big data analytics, and rendering complex 3D scenes.
Ready to unlock maximum power for your AI and rendering projects? Contact us for a custom configuration with NVIDIA GPUs.
Why CPU Alone Isn’t Enough: CPU vs GPU for AI and Rendering
To understand the importance of a GPU, it helps to compare it to a CPU. Imagine you need to assemble thousands of identical cars. A CPU is like a single highly skilled mechanic assembling one car at a time—very fast. A GPU, on the other hand, is like a massive assembly line with thousands of robotic arms, each performing a small repetitive task. Even if each arm is slower than the mechanic, their simultaneous work lets you assemble thousands of cars in a fraction of the time.
| Feature | CPU (Central Processing Unit) | GPU (Graphics Processing Unit) |
|---|---|---|
| Architecture | Optimized for complex sequential tasks | Optimized for massive parallel computing |
| Number of Cores | Few very powerful cores (e.g., 4–64) | Thousands of simpler cores (e.g., 2,000–10,000+) |
| Ideal for | Operating system management, databases, web servers | Machine learning, 3D rendering, data analytics, scientific computing |
| Speed (for AI) | 1x (Baseline) | 10x – 100x faster |
Main Use Cases for Dedicated GPU Servers
The versatility of dedicated GPU servers makes them ideal for a wide range of compute-intensive applications. Here are the main areas where our solutions make the difference.
1. Machine Learning and Deep Learning
Training deep learning models requires processing huge datasets through complex matrix computations. NVIDIA GPUs, with specialized libraries such as CUDA and cuDNN, accelerate this process exponentially, cutting training times from weeks to days—or even hours. Whether you’re working on facial recognition, natural language processing (NLP), or autonomous driving, a GPU server is essential.
2. 3D Rendering and Animation
Animation studios, architects, and designers use rendering software such as Blender, V-Ray, and OctaneRender, which leverage GPU power to generate photorealistic images and complex animations. A dedicated GPU server lets you build powerful and scalable render farms, dramatically reducing production time and enabling faster creative iterations.
3. Scientific Computing and Simulations
In scientific research, GPUs are used for complex simulations in fields such as computational fluid dynamics (CFD), bioinformatics (genomic analysis), and climate modeling. The ability to parallelize computations allows researchers to analyze complex scenarios and obtain results in reasonable timeframes, accelerating scientific discovery.
4. Big Data Analytics and Data Science
Analyzing terabytes of data to extract insights requires massive compute power. Platforms like NVIDIA RAPIDS leverage GPUs to accelerate the entire data science workflow—from SQL queries to data visualization—delivering superior performance compared to CPU-only solutions.
Our Offer: Customizable Dedicated Servers with NVIDIA GPUs
We understand that every project has unique requirements. That’s why we don’t offer one-size-fits-all packages, but a tailor-made configuration service. Starting from our solid server platforms (AMD Ryzen, Intel Xeon, AMD EPYC), we can integrate the NVIDIA GPUs best suited to your workload and budget.
All our dedicated servers, including GPU servers, come with our standard advantages:
- Supermicro Enterprise Hardware: Guaranteed reliability and performance.
- Milan Datacenter: Maximum speed and low latency for Italy and Europe.
- Proprietary DDoS Protection: Always-on security included—crucial for mission-critical services.
- 2 Gbit/s Bandwidth: Ultra-fast connectivity to move your data.
- 24/7 Italian Support: A team of experts always available.
Don’t let a lack of computing power limit your innovation. Explore our server platforms and request a GPU configuration.
The Future Is Parallel
In a world increasingly driven by data and artificial intelligence, having the right tools isn’t optional—it’s essential. A dedicated GPU server is not just hardware, but an innovation accelerator that can make the difference between an idea and a successful product. If you’re ready to take your projects to the next level, our team is here to help you build the perfect solution.
Which NVIDIA GPUs do you offer?
We offer a wide range of professional NVIDIA GPUs, from GeForce RTX series to the most powerful NVIDIA A-series (formerly Tesla). Since every project has specific needs, we work directly with the customer to select the GPU with the best price/performance ratio for the workload—whether it’s AI training, inference, or 3D rendering. Contact us for a consultation and a custom configuration.
Can I install more than one GPU in my dedicated server?
Absolutely. Many of our server platforms, especially those based on AMD EPYC, support multi-GPU configurations. This is ideal for building powerful deep learning training machines or high-performance render farms. We can configure servers with 2, 4, or even more GPUs depending on your needs.
Which software and frameworks are compatible with your GPU servers?
Our servers are compatible with all major machine learning and deep learning frameworks such as TensorFlow, PyTorch, Keras, and JAX, as well as the full NVIDIA CUDA stack (cuDNN, TensorRT, etc.). For rendering, we support software like Blender, V-Ray, OctaneRender, and Redshift. We can pre-install the operating system and NVIDIA drivers to get you up and running in no time.
Is a GPU server also suitable for gaming?
Yes, a dedicated server with a powerful NVIDIA GPU is excellent for hosting game servers for titles that require high graphics performance or for cloud gaming services. The combination of a fast CPU, NVMe storage, and a dedicated GPU ensures smooth, low-latency gameplay for all connected players.
What’s the difference between using a GPU server and a cloud service like AWS or Google Cloud?
The main difference is cost and control. With a dedicated server, you get exclusive resources and fixed monthly costs, which are typically far more cost-effective for continuous workloads (24/7). Cloud services, while flexible, can become extremely expensive for training complex models. With our dedicated server, you have full control over hardware and software—no surprise costs.
Do you provide support for setting up a machine learning environment?
Our standard support includes initial server setup, operating system installation, and NVIDIA driver installation. While we don’t provide consulting on AI model development, our systems support team is available 24/7 to ensure your hardware and network infrastructure are always performing and accessible, allowing you to focus on your work.
What is the additional cost for a GPU?
The cost varies significantly depending on the GPU model. A high-end consumer GPU may add €50–€100/month, while a datacenter-grade professional GPU like an NVIDIA A100 can cost substantially more. For this reason, we prefer to provide a custom quote based on your specific requirements and budget.
Can all of your dedicated servers be equipped with a GPU?
Most of our server platforms support adding GPUs. Platforms based on AMD EPYC and Intel Xeon are especially suitable due to their ability to support multiple cards and the large number of available PCIe lanes. Contact us with your use case and we’ll recommend the best base configuration to start from.
