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GPU Computing

Achieve Computational Breakthroughs Through Our GPU Solutions

GPU computing utilizes graphics processing units for more than just rendering visuals, capitalizing on their ability to execute multiple tasks in parallel. Boasting thousands of cores, perfectly suited for handling complex, large-scale data sets and repetitive tasks efficiently.

This parallel processing capability is key for a broad spectrum of applications, from scientific simulations and data analysis to machine learning and graphics design. By working in tandem with CPUs, where the GPU takes on the heavy lifting for compute-intensive tasks, processing speeds are greatly enhanced.

Such a collaborative approach has cemented GPU computing as an essential component of high-performance computing (HPC) environments, particularly for powering AI-driven tasks and analyses in various fields. This synergy not only speeds up computations but also expands the potential for groundbreaking discoveries and innovations across industries.


The NVIDIA A100 80GB Graphic Card is a highly advanced GPU designed for the most demanding computational tasks.

Feature Specification
CUDA Cores 6912
Memory 80 GB HBM2e
Memory Bus Width 5120 bit
Multi-GPU Technology NVLink
API Supported OpenCL, OpenACC, DirectCompute
Number of GPUs 7
Host Interface PCI Express 4.0 x16
Power Supply Wattage 300 W
Power Connector 1x 8-pin
Cooler Type Passive Cooler
Form Factor Plug-in Card
Platform Supported PC, Linux
Environmental Certification RoHS
MPN 900-21001-0020-100

Collaboration GPU & CPU

In GPU computing, the CPU oversees the program while offloading tasks to the GPU that are suited for parallel processing, such as:

  • Complex mathematical computations and numerical simulations

  • Advanced image and video processing tasks

  • Extensive data analysis involving large datasets

The GPU steps in to manage specific operations, distributing them across multiple cores for simultaneous execution, thus enhancing overall processing efficiency.

A quick look at Supermicro’s X13 generation of GPU servers

Get a quick look at Supermicro's X13 generation GPU system. Supermicro's X13 portfolio features more than 15 system families optimized for tomorrow's data center workloads.

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Interested in harnessing the power of GPU computing for your projects? Our sales team is ready to assist you. Contact us to explore how our GPU Computing solutions can transform your computational capabilities.

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7 Benefits of GPU Computing

GPU computing delivers key advantages across various sectors, highlighted as follows:

With thousands of cores, GPUs excel at parallel processing, handling numerous calculations at once.

This technology speeds up the analysis of complex workloads, crucial for time-sensitive tasks like medical imaging or financial trading.

Scaling GPU solutions is straightforward; adding more GPUs or clusters expands system capabilities efficiently.

Accelerates AI model training, enabling the development of sophisticated AI applications.

Vital for producing high-quality 3D graphics and visual effects in gaming, simulations, and virtual reality.

Compared to CPU-only systems, GPUs achieve similar computational power more economically, reducing hardware and energy costs.

Incorporating GPUs into HPC clusters significantly enhances their calculation speed, essential for demanding computational tasks across various sectors.

Programming Models and Memory Management

Developers leverage GPU programming models to utilize its parallel processing, with popular frameworks including:

  • CUDA: Nvidia's platform for parallel computing, offering tools and libraries

  • ROCm: AMD's open-source platform for GPU computing

  • SYCL: A C++ framework for developing applications on GPUs

  • OpenCL: An open standard that supports parallel programming across different brands

GPUs feature a unique memory hierarchy to manage data efficiently, transferring it from the CPU to the GPU's memory to minimize latency and maximize performance.