Glossary: Cybersecurity Terms & Definitions

Graphics Processing Unit (GPU)

What is a GPU?

A GPU, or Graphics Processing Unit, is an electronic chip originally conceived to speed up the rendering of images and animations on computers and gaming platforms. Over time, it has expanded to also be a high-performance processor well suited for data-intensive operations in areas such as artificial intelligence (AI), machine learning, and high-performance computing (HPC). Unlike a CPU—which has fewer but more sophisticated cores for sequential tasks—a GPU contains hundreds or even thousands of simpler compute engines that specialize in handling many numeric operations concurrently.  

Key Components

  • Cores: A single GPU can incorporate thousands of compute engines to process vast amounts of numeric computations simultaneously. 
  • Memory: Dedicated, high-bandwidth memory allows swift data transfers, enabling high throughput for very large AI models. 
  • Cooling Systems: Due to the power-intensive workloads, GPUs require robust cooling solutions, typically using fans or liquid-based methods to maintain performance.  

How a GPU Works

When a compute-heavy program runs, its tasks are broken into smaller workloads that the GPU’s compute engines handle in parallel. These workloads involve moving data between the off-chip memory and the GPU’s own memory through a high-speed interface such as PCI Express. Upon completion, results are forwarded to other system component for display or additional processing. This architecture is essential for real-time applications in both graphics rendering and data analytics.  

Benefits

  • Massive Parallelism: Having numerous compute engines allow GPUs to handle parallelizable tasks with impressive speed. 
  • Accelerated Performance: Offloading calculations to the GPU reduce CPU burden, improving overall system performance. 
  • Energy Efficiency: Although GPUs can consume significant power, they are still typically more energy-efficient at AI tasks, which leads to energy savings for those workloads. 
  • Versatility: While initially designed for graphics, GPUs now excel in wider variety of computing (GPGPU) domains, spanning everything from neural network training to complex simulations.  

Applications

  • Gaming and Visualization: GPUs power advanced rendering, ensuring immersive experiences and high frame rates. 
  • Artificial Intelligence: Training deep learning models typically involves repetitive matrix operations ideally suited to GPU acceleration. 
  • Scientific Research: Tasks like climate modeling or genomics benefit from GPU-based parallelism, significantly reducing the time to achieve results. 
  • Finance and Analytics: GPUs enable rapid processing of large datasets, supporting real-time risk assessments or algorithmic trading. 
  • Media and Content Creation: Movie and animation studios rely on GPU-accelerated rendering to generate high-quality visuals more efficiently.  

Future Outlook

GPU manufacturers continually refine each generation with additional cores, better power efficiency, and faster memory. As data-driven fields expand—from edge computing solutions like autonomous drones to cloud-based analytics—GPUs will likely remain central to achieving real-time results and handling massive workloads. Their ability to accelerate machine learning, modeling, and simulation positions GPUs as a cornerstone of enterprise and scientific endeavors.  

In summary, GPUs offer extraordinary parallel processing capabilities that streamline both graphical and data-intensive processes. By facilitating rapid calculations, they are indispensable in modern environments where efficiency, scalability, and high-speed processing are critical.  

How F5 Enables AI Workloads

F5 leverages NVIDIA's BlueField DPUs to deliver cutting-edge networking and security solutions for GPU-powered AI workloads. With F5 BIG-IP Next for Kubernetes deployed on NVIDIA DPUs, organizations can optimize traffic management and enhance GPU utilization for demanding AI applications, including data ingestion, model training, inference, and agentic AI workflows. The solution provides seamless multi-tenancy, zero-trust security, and high-speed connectivity, enabling secure and isolated environments for diverse workloads across shared AI infrastructure. By offloading delivery and security functions to DPUs, F5 reduces CPU strain, ensuring efficient resource utilization and scalable performance. This integration equips organizations with a single control point for secure, high-performance operations, making it ideal for AI factories, hyperscalers, and service providers. 

Learn more how F5 delivers and secures enterprise AI deployments by visiting our AI webpage.