CPU vs. GPU: Which to Use and When?

When you use your computer for gaming, video editing, running LLMs, or just casual web browsing, two key components determine the system’s performance: the CPU and the GPU. Understanding the differences between CPUs and GPUs is crucial for choosing the right hardware and making the most out of what modern computing has to offer.

This comparison explores the roles of CPUs and GPUs, details their architectural differences, highlights their performance strengths, and explores which to use and when.

What are CPU and GPU?

CPU: The Central Processing Unit

The CPU is often described as the “brain” of the computer because it handles a wide variety of computational tasks necessary for the system to operate. It executes the instructions of computer programs through a fetch-decode-execute cycle. These processors manage everything from basic arithmetic and logical operations to system control and input/output management.

• Processors like the Intel Core series or AMD Ryzen series are designed for versatility, efficiently handling diverse workloads.

GPU: The Graphics Processing Unit

Initially developed to accelerate the rendering of images, videos, and 3D graphics, the GPU has evolved into a powerful processor optimized for parallel computation. It is the reason behind the Generate AI boom that the world has seen in the last 3 years. GPUs contain thousands of smaller, specialized cores designed to perform many calculations simultaneously. This massively parallel architecture makes them exceptionally efficient not only for graphic designing but for other tasks involving high computation.

• High-performance GPU examples include the NVIDIA Blackwell B200, RTX4090, and AMD Radeon RX series support AI development, gaming, graphic designing, and more.

CPU vs  GPU: Architecture Comparison

CPUs typically feature a relatively small number of powerful cores (ranging from 4 to 64 or more in high-end models). These cores are optimized for low-latency sequential processing: executing instructions one after another very quickly. Each core is complex, containing sophisticated control units, arithmetic logic units (ALUs), and multiple levels of cache memory for rapid data access.

In contrast, GPUs employ thousands of simpler cores designed to execute the same operation on multiple data points concurrently. This architecture prioritizes throughput – the total amount of work done over time. Instead of large caches per core, GPUs utilize high-bandwidth memory (VRAM) shared across cores, optimized for the large datasets common in graphics rendering and parallel computing.

Overall, CPUs excel at executing sequential tasks quickly (low latency) and handling diverse workloads efficiently. In contrast, GPUs are used for executing many similar tasks simultaneously (high throughput), ideal for parallelizable workloads.

CPU vs GPU: Performance Comparison

CPU performance is primarily influenced by its clock speed (GHz), the number of cores, and Instructions Per Clock (IPC), which measures how efficiently each core executes instructions. Higher values in these areas generally lead to faster processing for a wide range of tasks.

GPU performance is largely determined by its number of parallel processing cores (often called CUDA cores or Stream Processors), memory bandwidth (how quickly data can be moved to and from VRAM), and overall computational throughput (measured in FLOPS – Floating-Point Operations Per Second).

However, GPU performance can be limited by the CPU if the CPU cannot supply data fast enough, creating a “roadblock,” particularly noticeable in gaming at lower resolutions (e.g., 1080p) where the CPU’s role is more pronounced.

CPU vs GPU: Energy and Cost Comparison

CPUs generally consume less power during typical desktop tasks or light workloads. GPUs, particularly high-end models, have higher peak power consumption due to their large number of cores and require more robust cooling solutions. However, for tasks they are optimized for, GPUs can complete the work much faster than a CPU, potentially leading to lower total energy consumed for that specific task despite higher instantaneous power draw.

• CPUs are available from under $100 for entry-level models to over $600 for high-end consumer chips, with server/workstation CPUs costing significantly more.
• GPUs with entry-level graphics solutions start around $100-$200, mid-range options typically fall between $300-$800, and high-performance models can exceed $1,500.

The best value depends on the primary use case. For general computing, a mid-range CPU is often sufficient. For intensive computation requirements or tasks that involve processing at breakneck speed investing more in the GPU usually yields greater performance benefits.

CPU vs GPU: Which to Use and When?

So far we have seen the key differences in the architecture and performance of the two processing units CPU and GPU. Let’s now understand which processor to work with and when.

Use CPUs for :

• running operating systems like Windows, macOS, Linux, and related system processes.
• general productivity tasks like handling office software, web browsing, and email clients.
• executing the primary code for most software applications.
• basic level gaming and controlling tasks like game state, AI behavior, physics calculations, and input processing.
• handling web server requests, database management, and virtualization.

Use GPUs for:

• intermediate to advanced gaming which would involve rendering complex 3D graphics, applying visual effects, and enabling technologies like ray tracing.
• multimodal content creation which involves tasks like video editing (rendering, encoding), graphic design, and 3D modeling.
• speeding up the training and inference of neural networks.
• running complex scientific simulations in fields like physics, finance, and bioinformatics.
• accelerating large-scale data analysis and visualization.

CPU and GPU: When to use them together?

Today most advanced systems use both processors collaboratively. Even as software development evolves there is a growing reliance on heterogeneous computing frameworks like OpenCL, CUDA, DirectX, and Vulkan. These frameworks help to assign tasks to the most suitable processor: sequential parts to the CPU and parallel parts to the GPU to maximize the overall performance.

CPU vs GPU: Future Outlook

Presently, the distinction between CPU and GPU capabilities is evolving. With the progress in the field of Generative AI, there is an increasing need for building stronger computational systems. CPUs are incorporating more powerful integrated graphics (iGPUs), reducing the need for discrete GPUs in mainstream systems. GPUs are also becoming more programmable for general-purpose tasks (GPGPU computing).

Additionally, many specialized processors are making their presence felt:

• Neural Processing Units (NPUs): These processors are optimized for efficient AI inference on client devices.
• Tensor Processing Units (TPUs) and other AI Accelerators: These custom hardware are becoming popular for large-scale AI training and inference in data centers.

These specialized units work alongside CPUs and GPUs to create more efficient and powerful computing systems tailored to specific workloads.

CPU vs GPU: Summary

The following table is a summary of the key differences between CPU and GPU.

Conclusion

The CPU and GPU are fundamental, complementary components of modern computers. The CPU provides the versatile, low-latency processing required for general system operation and sequential tasks, while the GPU delivers the massive parallel throughput needed for graphics and data-intensive computations. Understanding their distinct strengths and how they work together is key to building or selecting systems that meet specific performance needs.

As computing continues to evolve, the interplay between these core processors and emerging specialized units will drive innovation across gaming, scientific research, artificial intelligence, and countless other fields. Recognizing the role of each processor allows users to make informed decisions and better utilize the technology shaping our digital world.