This form logs you into your management portal account. To access your help desk account, click here and use the form to the right of the news.
This form logs you into your management portal account. To access your help desk account, click here and use the form to the right of the news.
Powerful Dedicated Servers with GPUs Designed for Deep Learning, Machine Learning, & AI Research
For many tasks, such as deep learning (also known as deep structured learning or hierarchical learning), a CPU is no longer enough. In these cases, a GPU will actually help you perform operations significantly faster. This is primarily because with a modern GPU you’ll be able to run many more threads, a common CPU may have 16 cores while a common GPU has over 4000 cores. These cores are much simpler and cannot do as much a s CPU, but they don’t have to in this case, meaning a GPU allows you to train your neural network much faster.
Request a GPU Dedicated Server Discussion
Steadfast, a Colohouse Company, GPU dedicated servers can support a wide array of operating systems, such as Windows and most Linux distributions, and thus support most known machine learning and neural network libraries (i.e. TensorFlow, Keras, and Microsoft Cognitive Toolkit).
Our GPU dedicated servers can support a wide array of operating systems, Windows as well as most Linux distributions, and thus support most known machine learning and neural network libraries, such as TensorFlow, Keras, and Microsoft Cognitive Toolkit.
Cloud GPU vs GPU Dedicated Server
In most situations, the added abstraction layer of virtualization (the basis of cloud server services) offers a lot of value and flexibility, but in this situation, when you’re trying to achieve the highest performance possible, it just gets in the way. Getting direct access to the hardware will help assure you’re getting the most out of the expensive hardware you’re paying for, while also reducing your risk of bugs or resource contention. Why go with a cloud based solution if it is going to cost just as much, but provide more problems and lower performance? Go with a dedicated solution and get direct access to all the power and features of your GPU.
Nvidia RTX 2080 Ti vs Titan RTX vs Tesla V100
The best value GPU for deep learning is now the Nvidia RTX 2080 Ti. Yes, there are faster GPUs out there, such as the Titan RTX and the Tesla V100, but the RTX 2080 Ti offers by far the best value. While the Titan RTX is 8% faster and and Tesla V100 is 25% faster (3rd party testing with FP32), we can provide the Nvidia RTX 2080 Ti systems at less than half the price. As an example, you can get 4x RTX 2080 Ti cards in one system for about the same price as a single Tesla V100 and get 3 times the performance. In the end, that means if you’re looking for the best bang for the buck, the Nvidia RTX 2080 Ti is it.
There are situations where the Nvidia RTX 2080 Ti may not fit your needs, and is why we offer other options. If you need more than the 11GB of memory provided by the Nvidia RTX 2080 Ti the Titan RTX might be worth the extra money for you. Then if you’re needing to FP64 compute (if you don’t know if you do, you probably don’t) you will need to go with the Tesla V100.