GPU Computing for HPC & AI – NVIDIA GPUs for Maximum Compute Performance

GPU Computing with NVIDIA & AMD GPUs – Performance of Modern Accelerators

The NVIDIA B300, based on the Blackwell Ultra architecture, is designed for demanding AI workloads such as Large Language Models, AI Training, Inference and Reasoning. With 288 GB HBM3e and high memory bandwidth, it offers high memory capacity for large models and data-intensive AI applications.

NVIDIA B300 – Maximum Performance for AI Data Centers

The NVIDIA B300, based on the Blackwell Ultra architecture, is designed for demanding AI workloads such as Large Language Models, AI Training, Inference and Reasoning. With 288 GB HBM3e, it offers high memory capacity for large models and data-intensive AI applications. In NVIDIA HGX systems, multiple B300 GPUs can be connected to form highly scalable AI platforms.

• VRAM: 288 GB HBM3e
• CUDA Cores: 18,944
• Tensor Cores: 592 (5th Gen.)
• TDP: 1,100 W

AMD Instinct MI355X – Top Performance for HPC and AI

The AMD Instinct MI355X, based on the CDNA 4 architecture, is designed for demanding AI Training, Inference, Large Language Models and High Performance Computing. With 288 GB HBM3E and up to 8 TB/s memory bandwidth, it offers high performance particularly for memory- and compute-intensive AI and HPC workloads.

• VRAM: 288 GB HBM3e
• Stream Processors: 16,384
• Matrix Cores: 1,024
• TDP: 1,400 W

With power consumption in the kilowatt range, current high-end GPUs place high demands on power supply, cooling and server design. Particularly with GPUs such as the NVIDIA B300 and AMD Instinct MI355X, power supply and heat dissipation must be dimensioned accordingly. For high GPU densities, specially designed GPU servers and modern cooling concepts are used – for example OCP Servers and Liquid Cooled Servers.

Applications of GPU Computing in HPC, AI & Data Science

Modern GPUs feature different specialized computing units and, due to their massively parallel architecture, are suitable for numerous compute-intensive applications. Typical applications of GPU Computing include:

• High-Performance Computing: For complex scientific and technical simulations in which huge amounts of data need to be processed in parallel – for example in genome sequencing, molecular dynamics or climate research.
• High-Performance Trading: In the financial sector, GPUs accelerate compute-intensive tasks such as market analysis, Monte Carlo simulations, risk models, backtesting and AI-based forecasting models.
• GPU Rendering: For 3D artists, architects and animation studios creating photorealistic images and animations. GPUs reduce rendering times from hours to minutes and enable real-time visualizations.
• Video Transcoding: In professional video editing and the conversion of video formats (transcoding), GPU Computing enables smooth processing of high-resolution material (4K/8K) and dramatically accelerated exports.
• Deep Learning: The massively parallel architecture of GPUs is optimized for the compute-intensive matrix and tensor operations that form the foundation of deep learning algorithms. This results in a fundamental acceleration of both compute-intensive training and efficient inference (the application of already trained models).

Frequently Asked Questions About GPU Computing 

What is GPU Computing?

GPU Computing (also GPGPU – General-Purpose Computing on Graphics Processing Units) is the use of the massively parallel architecture of a graphics processing unit (GPU) to accelerate general-purpose computing tasks. Instead of processing complex tasks one after another (serially, like a CPU), a GPU can perform thousands of calculations simultaneously (in parallel), making it ideal for data-intensive applications.

Why is GPU Computing crucial for AI and Deep Learning?

Training AI models, particularly in Deep Learning, is based on extremely compute-intensive matrix and tensor operations. The architecture of a GPU is designed precisely for this type of parallel mathematical computation. Specialized computing units for matrix and tensor operations enable significant acceleration of the training and inference of modern AI models.

Do I need specialized solutions for professional GPU Computing?

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

• Specialized GPUs: Use of professional NVIDIA Data Center GPUs such as B200, B300, H200 or AMD Instinct MI350X/MI355X with high HBM memory capacity and specialized computing units for AI and HPC workloads.
• Power Supply & Cooling: Current high-end GPUs can reach power consumption of more than 1,000 W per accelerator. GPU servers must therefore be specifically designed for the GPUs used in terms of power supply, cooling and thermal design.
• Scalability: GPU servers can combine multiple GPUs within a single system and scale through GPU clusters to multi-node infrastructures.

GPU Computing Solutions from HAPPYWARE – Consulting, Planning & Implementation from a Single Source

We are happy to provide the right GPU Computing solution for every requirement. Here you can find an overview of our offerings:

• GPU Server Use the dedicated computing performance of GPU Servers configured for you to provide graphics applications with even more computing resources. Benefit from full data sovereignty and cost efficiency in continuous operation compared to cloud solutions.
• GPU Cluster With our support, plan powerful GPU Clusters capable of handling GPU Computing tasks in a computing cluster with maximum efficiency.
• GPU Workstation Enormous computing performance in a compact space: For flexible GPU Computing, an individually configured GPU Workstation is a powerful solution.

HAPPYWARE offers GPU Servers, Workstations and Clusters based on current platforms from various leading manufacturers. Depending on the application, systems can be implemented with PCIe GPUs as well as high-density NVIDIA HGX or AMD Instinct platforms. For particularly powerful AI and HPC infrastructures, multi-GPU, multi-node and Direct Liquid Cooled solutions are also available.

High-End GPU Workstations – Powerful, Scalable and Versatile

For demanding computing tasks, high-end GPU Workstations are available as tower models with up to four GPUs. These systems are ideal for applications in Artificial Intelligence, Deep Learning, Simulation or High-Performance Rendering.

Network connectivity can be adapted to the respective infrastructure and workload. Depending on the system, current Ethernet or InfiniBand technologies are used. Particularly in multi-GPU and cluster systems, high network bandwidth and low latency are crucial for efficient communication between nodes.

GPU Computing – Computing Power for Matrix Operations

Anyone who has worked with GPU Computing or computer graphics programming knows that matrix operations form the basis of many calculations. This is precisely where the strength of graphics processing units (GPUs) lies: they perform such operations massively in parallel directly within the server.

Modern GPUs feature a large number of parallel computing units and are designed to perform many similar operations simultaneously. In addition to traditional graphics processing, GPUs are therefore used for general-purpose parallel computing, for example for matrix and tensor operations in AI, numerical simulations, data analysis and scientific computing. This use is referred to as GPGPU (General-Purpose Computing on Graphics Processing Units).

GPU Computing Solutions from HAPPYWARE – Professional Consulting and Implementation

Would you like to learn more about GPU Computing or are you interested in our specific solutions? Please feel free to contact our GPU Computing specialist Jürgen Kabelitz. He will be happy to provide you with individual advice.