High-performance servers for Machine Learning projects

Most popular machine deep learning tasks use NVIDIA GPUs, so, business owners tend to think about different moments before making a decision: What is the right solution for such a purpose? To get the answer, it is worth understanding the pricing and performance of different NVIDIA GPUs. Thus, to deal with large-scale projects requiring high computing power, reliable providers offer customers a service of comfortable renting servers for machine deep learning with appropriate software.

How to choose a dedicated server for deep learning?

As you know, the procedure of model training and deep learning is more expensive computationally, and it takes a much larger amount of power if compared to an already trained model. If the task is not expected to be under heavy load, then it is possible to operate not the most powerful machine deep learning server to save the budget. The most productive artificial intelligence servers for machine learning are more expensive than the average ones.

For an ordinary working version of the system (rack servers), you will be able to get by with part of the NVIDIA GPU resources, or one card can be used to run several samples. However, this solution is not suitable for training because of the memory structure, and each model uses a whole number of graphics accelerators to be trained.

A solution is considered as well-trained when the expected accuracy is achieved or when it is no longer improved by further training. Sometimes "bottlenecks" occur during the process. Auxiliary operations such as preprocessing and loading of images can consume a lot of CPU time - this means that the server configuration has not been balanced appropriately for that specific activity.

This is equally true for both dedicated and virtual servers solutions. This may be because one CPU is actually serving multiple instances in virtual models. A dedicated server has a complete CPU at its disposal but it may still be underpowered. In simple terms, in many cases, the capacity of the CPU matters, so that it can handle the initial processing of data.

Advantages of Hostkey’s servers for deep learning

Hostkey presents reliable machine deep learning hosting. In order to get your ideal equipment and software, you need to contact company experts, and they will help you with the selection. Our qualified technicians provide stable technical support by phone and mailing 24/7. They will always provide information about the application of software, frameworks, networks, different solutions. All maintenance of the rented computer and systems is done by Hostkey staff. We also offer a free trial period and regular pleasant bonuses and discounts. We can help you to find a perfect solution! So, hurry up to visit our website and order really perfect services!

What is a machine learning server?

The Deep Learning Server is a dedicated machine server that supports resource-intensive AI and deep learning workloads. It has high performance compared to traditional workstations through the use of multiple graphics processing units (GPUs), for example, NVIDIA.

The demand for the use of artificial intelligence has skyrocketed, leading to the development of products that can handle massive amounts of data and complex deep learning workflows. Security concerns have led to the growth of the market for specialized deep learning solutions that can handle resource-intensive AI workloads.

What is AI server?

The AI server is designed to support a network of real-time machine learning and inference systems that place high demands on the ratio of efficiency and power consumption despite the use of powerful NVIDIA GPUs. The server is great for deep learning with image and video analytics, as well as deep immersive methods, delivering unrivaled performance in a reasonably compact package of the solution. The AI Server supports GPUs (such as NVIDIA) and its framework can be flexibly configured for any scenario and inference requirement. The server can use one or more processors for making network functioning more stable.

Key features of the server are:

• - 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.