GPU Computing - NVIDIA GPUs for Ultimate Computing Performance

GPU Computing with NVIDIA GPUs – What Can Modern GPUs Do?

The top models of the NVIDIA H100 series, also known as Hopper GPUs, are among the most powerful graphics cards on the market. They are specifically designed for compute-intensive tasks such as large language models (LLMs) and enterprise AI training.

With up to 16,896 CUDA cores and 1,024 Tensor Cores, the NVIDIA H100 delivers impressive computing power:

• FP32 peak performance: up to 67 TFLOPS
• FP64 peak performance: up to 34 TFLOPS
• Tensor peak performance (TF32): up to 989
• TFLOPS training performance (FP16 Tensor): up to 3,958 TFLOPS

This makes the H100 ideal for models with more than 175 billion parameters – such as those used in AI research or generative AI.

With a power consumption of up to 700 watts, H100 GPUs are among the most energy-intensive models. For reliable operation, purpose-built servers and cooling solutions tailored to GPU performance are strongly recommended.

Use Cases for GPU Computing

As described above, the top-tier GPU model contains various computing units. This allows GPU computing servers to be used in the following areas:

• High-Performance Computing: For complex scientific and engineering simulations that require massive parallel processing of large datasets.
• High-Frequency Trading: In the financial sector, particularly in algorithmic trading, where split-second decisions can determine profit. GPUs are used to analyze vast amounts of real-time market data, calculate advanced predictive models, and execute trades with ultra-low latency to gain a competitive edge.
• GPU Rendering: For 3D artists, architects, and animation studios creating photorealistic images and animations. GPUs cut render times from hours to minutes and enable real-time visualizations.
• Video Transcoding: In professional video editing and format conversion (transcoding), GPU computing enables smooth handling of high-resolution material (4K/8K) and significantly faster export times.
• Deep Learning: The massively parallel architecture of GPUs is optimized for the matrix and tensor operations that underpin deep learning algorithms. This dramatically accelerates both training and inference processes for AI models.

Frequently Asked Questions about GPU Computing

What is GPU Computing?

GPU computing (also known as GPGPU – General Purpose Computation on Graphics Processing Units) refers to the use of a GPU’s massively parallel architecture to accelerate general-purpose computational tasks. Instead of processing tasks sequentially, a GPU can perform thousands of calculations simultaneously, making it ideal for data-intensive applications.

Why is GPU computing critical for AI and deep learning?

Training AI models—especially in deep learning—requires extremely compute-heavy matrix and tensor operations. The architecture of a GPU is designed specifically for these types of parallel mathematical operations. Specialized units further accelerate these tasks, reducing the training time for complex neural networks from months to days. Without GPU computing, modern AI development would be virtually impossible.

Do I need specialized solutions for professional GPU computing?

Yes. While consumer graphics cards already offer high performance, professional applications often require purpose-built GPU solutions. GPU systems from HAPPYWARE are designed for continuous operation and offer key advantages:

• Specialized GPUs: Deployment of NVIDIA GPUs such as the H100, optimized for compute workloads and equipped with features like increased VRAM and Tensor Cores.
• Power & Cooling: High-end GPUs have a high power draw (up to 700 watts). Our servers and workstations ensure stable power supply and adequate cooling to maintain consistent performance without throttling.
• Scalability: Our GPU servers and clusters support up to 16 or more GPUs in a single system for maximum computational performance.

GPU Computing Systems from HAPPYWARE

We are happy to provide tailored GPU computing solutions to meet your specific needs. Here’s an overview of our offerings:

• GPU Servers: Leverage dedicated computing power with custom-configured GPU servers to supercharge your graphics and compute applications.
• GPU Clusters: With our expert support, design powerful GPU clusters that execute GPU computing tasks efficiently across distributed systems.
• GPU Workstations: Massive computing power in a compact form – a custom-built GPU workstation is a flexible and powerful solution for local GPU computing.

HAPPYWARE offers GPU workstations and GPU servers based on platforms from Supermicro, ASUS, Tyan, and GIGABYTE – configurable with up to 16 NVIDIA GPUs. We also provide rack systems ranging from 1U with 4 GPUs to 10U supporting 16 GPUs. Using single-width GPUs, we can even build GPU computing systems with up to 20 cards.

High-End GPU Workstations – Powerful, Scalable, and Versatile

For demanding computational tasks, high-end GPU workstations are available in tower configurations with up to four GPUs. These systems are ideal for AI, deep learning, simulation, and high-performance rendering applications.

Networking options can be flexibly tailored to your infrastructure – from Gigabit Ethernet to FDR InfiniBand, offering bandwidths from 1 Gbit/s to 10 Gbit/s, depending on your application and performance requirements.

GPU Computing – Compute Power for Matrix Operations

Anyone working with GPU computing or graphics programming knows: many calculations are based on matrix operations. This is where GPUs shine – performing such operations in massively parallel fashion directly within the server.

In traditional graphics processing, each pixel or pixel region is assigned a compute core – the higher the resolution, the more shader or compute units are needed. This architecture, designed for graphics output, can be adapted with special software for general-purpose calculations – such as scientific simulations or AI workloads. In this context, we speak of GPGPU (General-Purpose Graphics Processing Unit).

GPU Computing Solutions from HAPPYWARE – Expert Advice and Implementation

Would you like to learn more about GPU computing or explore our custom solutions? Then feel free to contact our GPU computing specialist, Jürgen Kabelitz. He’ll be happy to assist you with tailored advice and expert support.