CPU vs GPU: How They Work and When to Use Them

Differences Between CPUs and GPUs

Let’s now zoom into the specific differences between CPUs and GPUs. A major one we discussed was serial vs. parallel computation, but there are other important ones.

Architecture and design

CPUs are built with fewer, more powerful cores optimized for single-threaded and sequential processing. These cores feature advanced control units, larger caches (L1, L2, and sometimes L3), and support for sophisticated instruction sets like x86-64 or ARM.

In contrast, GPUs have a massive array of smaller cores designed to handle parallel tasks. These cores operate under a Single Instruction, Multiple Data (SIMD) model, where the same operation is applied across multiple data points simultaneously.

A comparison of the GPU and CPU architecture. Image source.

Performance and efficiency

As you can imagine, the differences in architecture directly influence performance.

With their high clock speeds and advanced instruction handling, CPUs excel at low-latency tasks that require high precision and logical operations. Examples include operating system management, database queries, and running application threads. CPUs are also more versatile and capable of efficiently switching between diverse workloads due to their instruction decoding and branch prediction capabilities.

As mentioned, GPUs thrive in tasks where parallelism is key. Their ability to process multiple data streams simultaneously makes them indispensable for high-throughput operations, such as video encoding, scientific simulations, and neural network training. For instance, in deep learning, GPUs accelerate matrix multiplications and backpropagation, drastically reducing training times compared to CPUs.

The trade-off lies in the nature of the workload: CPUs outperform GPUs in sequential tasks, whereas GPUs dominate in data-heavy, parallel tasks.

Power consumption

GPUs generally consume more power due to their high core count and intensive parallel processing demands, while CPUs tend to have lower power requirements. 

A high-end GPU can consume several hundred watts under load, particularly during intensive operations like training machine learning models or rendering 4K graphics. Additionally, their high memory bandwidth requirements further increase power draw.

CPU features like dynamic voltage and frequency scaling (DVFS), low-power states (C-states), and efficient cooling mechanisms allow CPUs to effectively balance performance with power consumption. This makes CPUs more suitable for devices requiring longer battery life or lower thermal output, such as laptops and mobile devices.

Cost and availability

CPUs are widely available in consumer devices and are typically more affordable. Their broad utility and market saturation make them relatively inexpensive for consumers. Entry-level CPUs can cost under $100, while high-performance models for servers or workstations may range into the thousands. 

GPUs, especially high-performance models, are more expensive and commonly found in workstations, gaming systems, and high-performance computing environments. Consumer-grade GPUs for gaming may range from $200 to $1,000, while specialized GPUs for data centers or scientific research, such as NVIDIA’s A100 or AMD’s Instinct series, can cost tens of thousands of dollars.

The following table offers a detailed comparison of CPUs vs GPUs, making it easier to see their differences at a glance:

CPU Use Cases

Let’s briefly discuss some more specific use cases for CPUs:

• General-purpose computing: CPUs are perfect for everyday tasks, such as browsing the internet, using productivity applications, and using your computer for daily use. The majority of your computer’s workload is based on the CPU.
• Single-threaded applications: Tasks that require single-threaded processing, like specific financial calculations, are better suited for CPUs. Other single-threaded applications are word processors, browsers, and music apps.
• Operating system management: CPUs handle essential system processes, managing inputs, outputs, and background tasks that keep the system running smoothly.

GPU Use Cases

Let's briefly go over some GPU-specific use cases:

• Graphics and video processing: GPUs are designed to render high-quality graphics, making them indispensable in gaming, video editing, and graphics-intensive applications. This is possible because of their parallel processing capability, as I mentioned throughout the article. Many games with intensive graphics require a reasonably strong GPU to play without lagging or issues.
• Machine learning and AI: GPUs are often used to train machine learning models and process large datasets quickly, meeting the high-performance needs of AI algorithms. Due to the volume of calculations, they are especially necessary for projects utilizing neural networks. Since many calculations can be done in parallel, GPUs are a critical component of complex machine learning.
• Data science and scientific computing: GPUs can accelerate large-scale computations and matrix operations in data science, making them valuable for data-intensive analyses and simulations. Calculating things like tensors, image analysis, and convolutional layers requires massive computation. For example, Polars offers a GPU engine for query acceleration.

The Future of CPUs and GPUs

Both CPUs and GPUs continue to grow in power and complexity! Their futures show an increase in computational ability and widespread usage.

CPUs adapting to more cores

CPUs are increasing core counts to meet modern multitasking demands. This improves their serial computing capability and offers some parallel computing through multithreading.

Some trends in CPU development include:

• Increased core counts: Consumer-grade CPUs now often feature 8–16 cores, while server-grade processors can include 64 or more cores. This enables improved parallel computing capabilities through technologies like simultaneous multithreading (SMT) and hyper-threading.
• Heterogeneous architectures: Modern CPUs, such as Intel’s Alder Lake series or ARM’s big.LITTLE design, combine high-performance cores with energy-efficient cores to optimize both power consumption and task performance.
• Integration with specialized units: CPUs are increasingly integrating accelerators, such as AI inference engines or graphics processing units, directly onto the chip to enhance performance in specific workloads.

GPU development for general-purpose computing

GPUs are evolving to handle general-purpose tasks, making them increasingly versatile for data-heavy operations. Manufacturers like NVIDIA and AMD are investing in hardware-agnostic software ecosystems (e.g., CUDA, ROCm) to expand GPUs' applicability beyond graphics.

Many machine learning packages are now capable of using GPUs. Frameworks like TensorFlow and PyTorch now natively support GPU acceleration, dramatically reducing computation times.

Specialized processing units

The future of processing isn’t limited to just CPUs and GPUs. The rise of specialized processors, such as TPUs (Tensor Processing Units) and NPUs (Neural Processing Units), reflects a growing demand for task-specific hardware:

• Tensor Processing Units (TPUs): Developed by Google, TPUs are designed specifically for accelerating neural network computations. Unlike GPUs, TPUs focus on matrix-heavy operations and are optimized for large-scale AI tasks. If you have more questions, read the article on TPUs vs. GPUs.
• Neural Processing Units (NPUs): Found in mobile devices and edge computing, NPUs are built to handle AI-related tasks like image recognition, speech processing, and natural language understanding, all while consuming minimal power.
• FPGA and ASIC advances: Field-Programmable Gate Arrays (FPGAs) and Application-Specific Integrated Circuits (ASICs) are also gaining traction for their ability to be customized for specific applications, such as cryptocurrency mining, network packet processing, and real-time analytics.

Conclusion

CPUs and GPUs each bring unique strengths to computing: CPUs excel at general-purpose and precise tasks, while GPUs shine in parallel processing and data-heavy workloads. Understanding these differences helps data scientists, gamers, and professionals make informed hardware choices. 

If you’re interested in exploring how GPUs power deep learning models, check out Introduction to Deep Learning with PyTorch. For a broader understanding of AI technologies and their applications, the AI Fundamentals track provides a comprehensive overview. To dive deeper into the mechanics behind machine learning, explore the course Understanding Machine Learning.