GPU Servers and GPU Workstations
With Graphics Processing Units (GPU) in our workstations and servers, we increase the performance of your high-performance processes many times over. The additionally installed GPU acts as a coprocessor and accelerates the applications by taking over and processing computationally intensive segments with a lot of processing time from the CPU.
This is Why GPUs Make The Computer so Fast
A Graphics Processing Unit (GPU) basically handles computationally intensive 2D and 3D graphics calculations and is usually located on the graphics card or on the motherboard. The computing power of modern GPUs is comparable to that of CPUs. Their mode of operation supports a wide variety of graphics processing functions such as antialiasing, rendering, shading, mapping, alpha blending or fogging.
Graphics processors have many hundreds of arithmetic units (ALU), which they bundle in groups to form SIMD computer architectures. Each one of them runs the same graphics program in unison, computing vectors, endpoints, and fragments for a separate data stream.
Graphics processors have many hundreds of arithmetic units (ALU), which they bundle in groups to form SIMD computer architectures. Each one of them runs the same graphics program in unison, computing vectors, endpoints, and fragments for a separate data stream.
Full Service from Consulting to Integration
Initial Consultation
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Quotation
According to your plans, we will prepare an offer for your individual solution.
Assembly
After assembling all components, integration is carried out on site by our IT specialists.
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GPU-Computing
This kind of GPU architecture with many SIMDs is used primarily in computing computer graphics. Due to the parallel processing, the computing power of these graphics processors is several hundred billion computing operations per second. Graphics processors have an integration density built up in structure widths of less than 50 nm and consist of several hundred million transistors. GPU-accelerated computing is now used in conjunction with CPUs to significantly accelerate HPC, deep learning, analytics and engineering applications. Compute-intensive application parts are executed on the GPUs and the remaining tasks are handled by the CPU.