What is a GPU?

A graphics processing unit (GPU) is a highly parallel computing engine optimized for rendering graphics, accelerating artificial intelligence (AI) workloads, and processing compute-intensive tasks such as image processing and simulation. Designed with hundreds to thousands of smaller cores, a GPU excels at performing many operations simultaneously, making it ideal for high-throughput, data-parallel applications across graphics and AI.

Why is a GPU Important?

A GPU plays a critical role across a range of computing domains:


  • Graphics rendering: Powers responsive visuals in games, user interfaces, and immersive media.
  • Artificial intelligence (AI): Accelerates both training and inference for AI models across vision, language, and generative workloads.
  • High-performance computing (HPC): Enables parallel data processing for simulations, analytics, and scientific workloads.
  • Edge and embedded devices: Integrated GPUs drive responsive visual computing in smartphones, vehicles, and IoT systems.
  • Cloud and data centers: Virtualized GPUs support scalable and efficient remote computing for AI and visualization tasks.

How Does a GPU Work?

A GPU performs computations by dividing complex tasks into smaller parallel workloads, executed across multiple processing cores. This model is especially effective for applying the same instruction to large data sets, common in rendering, neural network training, and video encoding.


Each core of a GPU processes a portion of the data simultaneously. For example:


  • Rendering a 3D scene involves computing lighting and texture effects in parallel.
  • Training machine learning models requires running extensive matrix operations, which GPUs accelerate significantly through parallel execution.

What are the Key Components or Features?

  • Parallel architecture: Consists of many smaller, efficient cores that execute computations concurrently.
  • Shader cores: Specialized processors that handle rendering and graphics effects like lighting and shading.
  • Video memory: High-speed memory that supports fast data access for graphics and compute workloads.
  • Form factors:
    • Discrete GPUs: Standalone chips on separate boards, often with dedicated cooling and power.
    • Integrated GPUs (iGPU): Embedded into CPUs or SoCs, sharing memory with the system.
    • Virtual GPUs (vGPU): Software-defined GPUs used in cloud and virtualized environments.
  • Programmability: Support for graphics and compute APIs such as OpenCL and Vulkan.

GPU vs. CPU: What’s the Difference?

GPU and AI: Enabling Modern Intelligence

GPUs have become foundational to the development and deployment of artificial intelligence. Their massively parallel architecture makes them uniquely suited to:


  • Training deep learning models: GPUs accelerate backpropagation and matrix operations across large datasets, significantly reducing training time.
  • Running inference at scale: For applications like real-time image classification or voice recognition, GPUs execute trained models with low latency and high efficiency.
  • Enabling generative AI: From LLMs to diffusion-based image generation, GPUs provide the compute density needed for high-volume transformer model workloads.

Arm GPUs support popular AI inference frameworks through standard compute APIs like OpenCL and Vulkan, enabling AI acceleration on a wide range of edge and embedded devices.

FAQ

What’s the difference between a GPU and a CPU?

A CPU is optimized for sequential, general-purpose tasks, while a GPU is built for high-throughput, parallel processing.

What is a discrete GPU vs. an integrated GPU?

A discrete GPU is a separate chip with dedicated memory, while an integrated GPU shares resources with the CPU on a single chip.

Can a GPU be used for non-graphics tasks?

Yes. GPUs are widely used for AI, simulation, and general-purpose compute applications.

How does a GPU accelerate AI?

A GPU performs large volumes of calculations in parallel, significantly speeding up model training and inference.

Do Arm GPUs support compute as well as graphics?

Yes. Arm Mali and Arm Immortalis GPUs are designed for both rich graphics and compute workloads using open APIs.

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