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GPUs in machine learning servers can increase the speed of Deep learning training by hundreds of times, and it can allow you to employ more iterations, conduct more experimentation, and generally perform much deeper exploration.
Deep learning is a type of machine learning that uses artificial neural networks and deep learning servers to enable digital systems to learn and make decisions based on the data they receive.
Using machine learning servers allows AI systems to learn from experience with data, identify patterns, make recommendations, and adapt. Deep learning, together with AI servers, is specifically responsible for creating digital knowledge systems from examples, and then using this data to simulate human response, behavior and work.
Using a deep learning server is suitable for image, speech and emotion recognition systems, chat bots, and personal digital assistants. You will also need AI servers to organize online resources that are focused on improving customer experience and creating automated product recommendations.
Modern applications for scientific and research computing, big data analytics, machine learning, and artificial intelligence operate on huge and constantly growing data sets. For this reason, the computing power of traditional systems with an infrastructure around even the best CPU for data science is unable to provide a sufficiently high level of workload for loading, sorting, processing and other data operations.
Deep learning servers with a GPU can accelerate the processing of big data by hundreds of times, while the investment in such a solution is incomparably less than what would be required to similarly increase the capacity of a traditional CPU server.
The GPU deep learning server allows you to parallelize heavy workloads across multiple threads using thousands of cores in each GPU, and dramatically accelerate the entire process of working with data from start to finish.
Reducing the time for training neural networks, training models, analysis and other data operations gives freedom for more iterations in experiments, and as a result, for conducting more accurate and in-depth research in the same period of time. The deep learning GPU server for big data keeps your venture competitive, while saving on infrastructure costs and electricity bills.
Here is just a short list of the branches of science and business where the deep learning GPU server for big data provides a huge increase in processing power for heavy computing:
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GPU accelerated servers are already widely employed in compute-heavy industries such as in AI model training, deep learning, machine learning, heavy-load scientific data crunching, and more:
The newer the GPU generation NVIDIA RTX A6000 / A5500 / A5000 / A4000, the better: each new generation of GPU architecture brings improvements to the computing cores, adding functions to tensor cores and ray tracing cores, all of which ultimately significantly increase the system power density per GPU core. So, in some network training tasks, the advantages of the RTX30 GPU over the RTX20 allow you to double your calculation output!
The older the graphics card in each generation (RTX3090 -> RTX3080-> RTX3070), the better: not only does the older model have a larger number of computing cores, important here, but also it has a larger amount of video memory, which significantly affects the speed of calculation.
If the project budget allows for the use of several GPU servers, you can economize on network modeling by inputting smaller networks or reduced images with the use of a lower budget GPU system, and later the final calculations and full scaling of the model to the required parameters can be carried out on a powerful GPU server.
A GPU server for Data Science applications will be reliant on choosing the appropriate multi-core CPU, sufficient RAM and a fast SSD: ensure that other system components do not become a bottleneck for your workloads.
HOSTKEY specialists will be happy to help you create a unique individual GPU server taking into account your tasks and goals. We will advise you on the choice of hardware, software and assemble the server according to your requirements as soon as possible.
Regardless of the configuration, we guarantee the placement of your chosen GPU server in a reliable TIER III data center, with a stable high-speed Internet connection, flexible payment system and 24/7 technical support.
Delivery time from 15 minutes to 1 day. GPU passthrough allows to directly present an internal PCI GPU to virtual KVM machines. The GPU card is dedicated to the VM and cannot be used by other clients. GPU performance in virtual machines matches GPU performance in dedicated servers. Since we use large multi-card nodes for vGPU, virtual machines come at a cheaper price.
Looking for a non-standard OS? We have a wide selection of ISO images in our control panel. Or you can use your own image and install the OS via IPMI. All our servers are unmanaged. Administration services can be provided for a fee. Please contact us for any questions.
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