Rent the Best GPU for Machine Learning Servers

What’s included with machine learning servers

High-Performance Servers for Machine Learning Projects

HOSTKEY offers powerful GPU servers for machine learning and deep learning tasks, providing high performance and reliability. The range includes servers with various NVIDIA video cards, including A100, H100, RTX 4090 and others, which are ideal for processing complex calculations in the field of artificial intelligence.

For machine learning servers, solutions based on NVIDIA RTX 4090 and A100 are optimal, providing fast data processing and model training. If maximum performance is required for large-scale projects, then a deep learning server based on H100 or A100 will speed up the training of neural network algorithms.

HOSTKEY also provides servers for machine learning with pre-installed software, which simplifies infrastructure deployment and allows you to focus on development. Flexible rental rates make these solutions accessible to both startups and large companies.

How to choose a dedicated server for deep learning?

• For inference and small models. One video card (NVIDIA RTX 4090, A10G or T4) is usually enough. Virtual servers with partial access to the GPU help reduce costs.
• For training complex neural networks. Servers with several GPUs (A100/H100) are required. High bandwidth between cards (NVLink/InfiniBand) and high-performance CPUs are important.
• Performance optimization. A weak processor creates bottlenecks when loading data. Choose servers with multi-core CPUs (Xeon/EPYC) and fast NVMe drives.
• Resource balance. The GPU should not be idle due to a slow CPU. To speed up data preprocessing, use specialized libraries like CuPy.

Advantages of Hostkey’s servers for deep learning

• Professional selection of equipment. Our experts will help you choose the optimal machine learning server configuration and a set of software that is ideal for your tasks.
• 24/7 technical support. Qualified specialists are ready to answer questions about the operation of frameworks, network settings and software 24/7.
• Full service by Hostkey. We take on all work on supporting the rented machine learning server, ensuring uninterrupted operation.
• Free trial period. Try our services without obligation during the trial period.
• Favorable conditions. Special offers and discounts are available for our clients.

Key Features of HOSTKEY’s Machine Learning Servers

• Exceptional computing power. The solution provides performance through the use of modern CPU and NVIDIA GPU.
• Maximum placement density. Servers are run in modern cases, which, with the indicated performance, makes the calculation density indicator 2.5 times higher than that of conventional rack servers.
• Dedicated decoding module supports a large number of video stream channels for real-time transcoding and inference
• Configuration flexibility of the solution.

Use Cases for Machine Learning Servers

• Big data processing and analysis. Machine learning server is ideal for processing large amounts of information. It quickly performs complex analytical tasks, which is especially in demand in the financial sector and marketing research.
• Training neural networks. Training deep neural networks (CV, NLP, recommender systems) requires significant computing power. A machine learning server with several GPUs speeds up this process several times.
• Computer vision. Image and video recognition systems require special resources. Machine learning server provides the necessary performance for processing graphic data in real time.
• Natural language processing (NLP). Working with texts, chatbots and voice assistants is another area of application of machine learning server. The server copes with complex language models (for example, GPT or BERT).
• Scientific research. In medicine, bioinformatics and other sciences, machine learning server help analyze complex data and accelerate discoveries.
• Industrial IoT and telemetry. Machine learning server processes data streams from sensors, predicts equipment failures and optimizes production processes.
• Personalization and recommendation systems. Online retail and media platforms use machine learning server to analyze user behavior and create personalized offers.
• Cybersecurity. Detecting anomalies and protecting against cyberattacks require powerful computing. Machine learning server identifies threats in real time.

Pre-Installed Frameworks and Tools

• TensorFlow. Our machine learning servers come pre-installed with TensorFlow, a popular framework for creating and training neural networks. It is ideal for large-scale machine learning and deep learning projects.
• PyTorch. For researchers and developers who prefer flexibility, PyTorch is available on the servers. This framework is especially in demand in the academic environment and when developing new neural network architectures.
• Keras. Keras is pre-installed on the machine learning server as a high-level API for quickly prototyping models. It runs on top of TensorFlow and significantly speeds up the development process.
• JAX. For scientific computing and advanced research, JAX is supported on the servers. This framework is especially effective when working with differentiable programming.
• Scikit-learn. Classical machine learning algorithms are available through the pre-installed Scikit-learn. This is the optimal solution for tasks that do not require deep learning.
• CUDA/cuDNN support. All machine learning servers have full support for CUDA and cuDNN, which ensures maximum performance when working with GPUs. This is critical for accelerating deep learning computations.

Compare NVIDIA GPUs for Machine Learning

NVIDIA Tesla T4 is the optimal choice for a machine learning server with moderate requirements. With support for Tensor Cores and 16 GB of GDDR6 memory, this card delivers good performance for inference and small models at low power consumption.

NVIDIA RTX 4090 offers an excellent price-performance ratio for a machine learning server. With 24 GB of GDDR6X memory and improved CUDA cores, it is suitable for training medium-sized models and research tasks.

NVIDIA A100 is a professional solution for an enterprise-class machine learning server. With Multi-Instance GPU (MIG) technology and 80 GB of HBM2e memory, this card is ideal for large projects and distributed computing.

NVIDIA H100 represents a new generation of accelerators for machine learning servers. With support for FP8 and improved Tensor Cores, it delivers unprecedented performance for transformer models and LLM applications.

The choice of GPU depends on the tasks:

• For inference and prototyping, T4 or RTX 4090 are sufficient
• For industrial ML/DL, A100/H100 are better suited
• Scalable systems require multiple A100/H100 with NVLink

All of the listed solutions support CUDA, cuDNN, and major ML frameworks, making them ideal for deployment on a machine learning server.

Machine Learning Server Setup and Deployment

1. Selecting the optimal configuration

   The Hostkey team helps you select the ideal machine learning server for your needs. Our experts analyze the requirements for computing resources, data volume and budget, offering optimal solutions based on NVIDIA GPUs (T4, A100, H100) with a properly balanced CPU, RAM and storage.

2. Professional assembly and configuration of equipment

   All machine learning servers in Hostkey are assembled in our data centers in compliance with strict quality standards. We install GPUs in optimal PCIe slots, configure effective cooling and test the equipment under load to ensure stable operation.

3. Software installation and optimization

   Our engineers pre-install all the necessary components on the machine learning server: current NVIDIA drivers, CUDA Toolkit, cuDNN, as well as popular frameworks (TensorFlow, PyTorch). The system is immediately ready to work with GPU acceleration.

4. Start of work in 15 minutes

   Thanks to ready-made deployment templates, we can provide a configured machine learning server within 15 minutes after ordering. This includes full preparation of the hardware and software environment.

Hourly and Monthly Pricing Options

1. Pay-as-you-go (Hourly Billing)

   Ideal for testing and short-term projects:

   • NVIDIA RTX 4090 – €0.35/hour
   • NVIDIA A100 80GB – €1.20/hour
   • NVIDIA H100 80GB – €2.50/hour

   Benefits:

   • Flexibility – pay only for the time you use
   • Instant access to GPU resources
   • Possibility to test different configurations

2. Monthly Subscription (Discount up to 30%)

   Optimal for long-term projects and production:

   • NVIDIA RTX 4090 – €420/month (≈ €0.58/hour)
   • NVIDIA A100 80GB – €1,440/month (≈ €2.00/hour)
   • NVIDIA H100 80GB – €1690/month (≈ €2.50/hour)

   Benefits:

   • Significant savings over long-term use
   • Guaranteed resource availability
   • Priority technical support

Why Use a Dedicated GPU Server for Machine Learning?

Using a dedicated machine learning server is an investment in the stability, performance and security of your AI projects, which quickly pays off due to operational efficiency and the absence of hidden costs.

• High performance for complex tasks. A machine learning server with dedicated GPUs provides tens of times faster computing speeds compared to regular servers. This is critical for training deep neural networks and processing big data, where every minute of equipment operation matters.
• Long-term cost optimization. Although renting a machine learning server requires investment, it is more cost-effective than using cloud solutions under constant high loads. You pay only for the resources you need without overspending on unnecessary services.
• Full control over the infrastructure. A private machine learning server gives you the freedom to choose the hardware configuration, software versions, and security systems. This is especially important for projects with special requirements for compatibility or data protection.
• Guaranteed resource availability. Unlike cloud solutions with shared resources, a dedicated machine learning server provides stable performance without "neighbors" that can affect your computing. This eliminates unpredictable delays in critical processes.
• Data security and privacy. When working with sensitive information, the machine learning server offers an isolated environment where all data is under your complete control. This complies with the strict requirements of GDPR, HIPAA and other information security standards.
• Scalability for growing needs. Modern machine learning servers allow you to gradually increase capacity — add GPUs, increase memory and storage. This makes them an ideal solution for startups and fast-growing companies.
• Support for professional equipment. Machine learning servers support specialized technologies such as NVLink for connecting GPUs, high-speed SSDs and RDMA infrastructure, which significantly speeds up data exchange between system components.