CPU vs. GPU: Making the Most of Both1

Central Processing Units (CPUs) and Graphics Processing Units (GPUs) are fundamental computing engines. But as computing demands evolve, it is not always clear what the differences are between CPUs and GPUs and which workloads are best suited to each.

Know When to Rely on a CPU and When to Choose a GPU

  • CPUs and GPUs each have unique strengths that will allow them to play an integral role in meeting the computing needs of the future.

  • GPUs are an important part of Intel’s products today in the form of integrated and discrete GPUs.

  • While CPUs remain critical, a growing number of data center workloads can benefit from GPUs.



Whether for deep learning applications, massive parallelism, intense 3D gaming, or another demanding workload, systems today are being asked to do more than ever before. A central processing unit (CPU) and a graphics processing unit (GPU) have very different roles. What are CPUs used for? What are GPUs used for? Knowing the role that each device plays is important when shopping for a new computer and comparing specifications.

What Is a CPU?

Constructed from millions of transistors, the CPU can have multiple processing cores and is commonly referred to as the brain of the computer. It is essential to all modern computing systems as it executes the commands and processes needed for your computer and operating system. The CPU is also important in determining how fast programs can run, from surfing the web to building spreadsheets.

What Is a GPU?

The GPU is a processor that is made up of many smaller and more specialized cores. By working together, the cores deliver massive performance when a processing task can be divided up and processed across many cores.

What Is the Difference Between a CPU and GPU?

CPUs and GPUs have a lot in common. Both are critical computing engines. Both are silicon-based microprocessors. And both handle data. But CPUs and GPUs have different architectures and are built for different purposes.

The CPU is suited to a wide variety of workloads, especially those for which latency or per-core performance are important. A powerful execution engine, the CPU focuses its smaller number of cores on individual tasks and on getting things done quickly. This makes it uniquely well equipped for jobs ranging from serial computing to running databases.

GPUs began as specialized ASICs developed to accelerate specific 3D rendering tasks. Over time, these fixed-function engines became more programmable and more flexible. While graphics and the increasingly lifelike visuals of today’s top games remain their principal function, GPUs have evolved to become more general-purpose parallel processors as well, handling a growing range of applications.

What Are Integrated Graphics?

Integrated or shared graphics are built onto the same chip as the CPU. Certain CPUs can come with a built-in GPU instead of relying on dedicated or discrete graphics.

Integrated graphics processors offer several benefits. Their integration with CPUs allows them to deliver space, cost, and energy efficiency benefits over dedicated graphics processors. They bring the power to handle the processing of graphics-related data and instructions for common tasks like exploring the web, streaming 4K movies, and casual gaming.

Such an approach is most often employed with devices for which a compact size and energy efficiency are important, such as laptops, tablets, smartphones, and some desktops.

Accelerating Deep Learning and AI

Today, GPUs run a growing number of workloads, such as deep learning and artificial intelligence (AI). A GPU or other accelerators are ideal for deep learning training with neural network layers or on massive sets of certain data, like 2D images.

Deep learning algorithms were adapted to use a GPU-accelerated approach. With acceleration, these algorithms gain a significant boost in performance and bring the training time of real-world problems to a feasible and viable range.

CPUs and the software libraries that run on them have evolved over time to become much more capable for deep learning tasks. For example, through extensive software optimizations and the addition of dedicated AI hardware—such as Intel® Deep Learning Boost (Intel® DL Boost) in the latest Intel® Xeon® Scalable processors—CPU-based systems have enjoyed improvements in deep learning performance.

For many applications, such as high-definition-, 3D-, and non-image-based deep learning on language, text, and time-series data, CPUs shine. CPUs can support much larger memory capacities than even the best GPUs can today for complex models or deep learning applications (e.g., 2D image detection).

The combination of CPU and GPU, along with sufficient RAM, offers a great testbed for deep learning and AI.

Decades of Leadership in CPU Development

Intel has a long history in CPU innovation beginning in 1971 with the introduction of the 4004, the first commercial microprocessor completely integrated into a single chip.

Today, Intel® CPUs let you build the AI you want, where you want it, on the x86 architecture you know. From high performance Intel® Xeon® Scalable processors in the data center and cloud to power-efficient Intel® Core™ processors at the edge, Intel delivers a CPU to match any need.

The Intelligent Performance of 13th Gen Intel® Core™ Processors

13th Gen Intel® Core™ processors leverage performance hybrid architecture with faster Performance-cores (P-cores) and more Efficient-cores (E-cores) with industry-leading tools to maximize performance and multitasking capabilities.

Select 13th Gen Intel Core processor-based laptops may include Intel® Iris® Xe graphics or Intel® Iris® Xe MAX dedicated graphics—the first discrete GPU powered by Intel Xe architecture. With Intel® Iris® Xe MAX dedicated graphics, you get a huge leap forward in thin and light notebooks, as well as greater performance and new capabilities for enhanced content creation and gaming.

Intel® Iris® Xe graphics feature Intel® Deep Learning Boost-powered AI for better content creation, such as photo and video editing, as well as low-power architecture for longer battery life.

Intel Discrete GPUs

Intel offers three discrete GPU options.

Intel® Iris® Xe MAX Dedicated Graphics is a discrete GPU with graphics card options for laptops and desktops. Based on Xe architecture, you get even more performance and new capabilities such as Intel® Arc™ Control for enhanced content creation and gaming.

The Intel® Data Center GPU is a GPU that supports emerging technologies, such as AI, rendering, analytics, and simulations. It also adds powerful parallel processing capabilities to data center CPUs.

Create compelling content, captivate your audience, and supercharge your gaming experience with the latest high-performance graphics solution, Intel® Arc™ graphics. Built on Xe HPG microarchitecture, Intel Arc graphics enable built-in machine learning, graphics acceleration, and ray tracing hardware for laptops, desktops, and professional workstations.

Today, it is no longer a question of CPU vs. GPU. More than ever, you need both to meet your varied computing demands. The best results are achieved when the right tool is used for the job.