- Functions
- Architecture
- Working
- Processing
- Compatibility
- Uses
- Price
- Pros and Cons
- Which One to Choose?
- Future
CPUs and GPUs are both silicon-based computing engines used for processing. CPUs are the most fundamental part of computer hardware, while GPU speeds up processing power. Enterprises employ both CPUs and GPUs to deal with resource-intensive tasks and heavy application workloads. Many checklists and evaluations must be done before choosing a CPU or GPU for the system like costs and compatibility. This blog highlights the differences between CPUs and GPUs to help you understand their importance and application in enterprises
Differences Between CPU and GPU
The following section will highlight the differences between CPU and GPU in a tabular format below.
| Feature | GPU (Graphics Processing Unit) | CPU (Central Processing Unit) |
| Parallel Processing | High parallel processing power | Limited parallel processing capabilities |
| General Purpose | Less versatile for general computing tasks | Highly versatile for a wide range of tasks |
| Performance | Excellent for graphics rendering and deep learning tasks | Adequate for general tasks, but less efficient for specific high-performance tasks |
| Power Consumption | Higher power consumption | Generally lower power consumption |
| Cost | Generally more expensive | Generally less expensive |
| Complexity | Requires specialized knowledge to utilize fully | Easier to use and program for general tasks |
| Integration | May require additional hardware integration | Usually integrated with other system components |
| Thread Handling | Can handle multiple threads simultaneously | Limited in handling multiple threads simultaneously |
| Computational Speed | Significant improvement in computational performance for specific tasks | Moderate computational performance |
| Single-thread Performance | Limited single-thread performance | Superior single-thread performance |
GPU vs CPU: Operation and Function
The Central Processing Unit (CPU) processes all critical information and executes commands to run operations smoothly. In contrast, graphics processing units (GPU) or graphics cards specialize in uni tasks like designing or processing graphical data such as rendering. The operation focus of the CPU is on low latency to process a high volume of instruction with minimal delays, measured in clock cycles.
An increase in latency can result in increased application load times and failures. On the other hand, GPUs focus on high throughput, maximizing the number of similar instruction executions per clock cycle. Low throughput may result in CPU bottlenecks and lower performance.
GPU vs CPU: Design Architecture
CPU processes data in sequence due to multiple powerful cores and high clock speed, suitable for multitasking with minimal latency. It consists of a Control Unit (CU), a Clock, an Arithmetic Logic Unit (ALU), Registers, Caches, and Buses. In contrast, GPU uses cores to perform multitasking operations, depending less on fast memory access or processor cache as in CPU.
It comprises Processor Clusters (PC), Streaming Multiprocessors (SM), Layer-2 cache, and DRAM. Unlike CPUs, GPUs excel in parallel processing using a large number of cores and multithreading operations.
GPU vs CPU: Working
GPUs contain microprocessors, each with a shared memory block, processors, and corresponding registers. They come in two types: discrete (standalone) or integrated chips. Discrete GPUs focus on graphics or dedicated processing for tasks like ML or complex simulation. Integrated GPUs combine CPU and GPU on a single chip or iGPU to handle everyday computing tasks like Xeon CPUs for servers.
In contrast, CPUs commonly known as the computer brain execute instructions while performing necessary calculations to run computer programs. CPU fetches instructions from memory to process data, decodes, executes it, and stores the results in cache, system memory, or registers. The multiple cores multitask and execute instructions independently to increase the processing power.
GPU vs CPU: Processing
Two types of processing take place in CPU and GPU: Parallel and Serial processing. Parallel processing focuses on multiple tasks at a time, while serial processing performs one task at a time. CPUs use a single-core processing and execute instructions serially via a program counter which determines the program execution order. Meanwhile, GPUs are more suitable for parallel processing, reducing program load time.
GPU architecture allows multiple processing streams simultaneously, increasing computer system calculation speed and throughput. It’s important to remember while GPUs process data faster due to parallelism, the CPU still has the advantage.
CPUs can process large instruction sets to manage every computer input and output which a GPU can’t. An individual CPU core is more powerful than an individual GPU core, but the shared number of GPU cores and parallel processing gives them an edge.
CPU vs GPU: Graphics and Image Processing
CPUs perform complex and versatile calculations but don’t have graphics or image processing power like GPUs. Today, many CPUs have integrated graphics or iGPU without a separate memory, making them less effective in resource-intensive processing than GPU.
In contrast, GPUs or discrete graphics cards are separate from the processors and perform only graphical and image processing using dedicated memory. Enterprise encodes and decodes images and graphics proceeding in videos or games at faster speeds via GPUs.
GPU is a separate component and occupies one or two PCIe slots depending on the form factor, it consumes more power than CPUs. Most high-performing GPUs require air or liquid cooling to function effectively and dissipate heat from PCs and servers as well.
CPU vs GPU: Compatibility
Most enterprises ignore CPU and GPU compatibility with their computer systems. It’s crucial to compare the benchmarks of each computer component such as memory (RAM), GPU, and CPU. Because systems can experience bottlenecks (low performance) if the CPU or GPU is not up to par with the other. CPU bottlenecks occur if the RAM, SSDs, or HDDs are not up to the mark with the CPU. Sometimes, you can experience lags or green pixels on the LCD or LED screens.
This is because the GPU, CPU, or LED can’t process the video requirements, breaking down during operation. You can easily calculate each component’s compatibility using online bottleneck calculators for free. However, these tools only provide you with an average estimation and should not be relied on completely.
CPU vs GPU: Applications
CPU and GPU find many applications in businesses due to their ability to solve complex calculations. Let’s explore some applications of CPU and GPU in tech industries workloads.
GPU vs CPU: Compiling Codes
In compiling large codebases the CPU performance is more important than GPUs due to more memory cache. The CPU processing depends on core counts, clock speed, and cache memory, affecting the speed of compiling codes.
GPU vs CPU: Neural Network
Neural networks learn from big databases by creating conditions for the human brain. CPUs can train smaller-scale networks only as the network expands GPUs are needed for more processing power. Neural networks have three or more layers to run and execute tasks independently, similar to GPU parallel processing. This gives GPU an edge over CPUs and is ideal for neural network applications.
GPU vs CPU: Machine Learning
Machine Learning (ML) requires an input of large continuous data sets to improve algorithm accuracy. CPUs aren’t effective for data-intensive machine learning applications yet a budget-friendly alternative to GPU for less intensive ML tasks. GPU parallel processing allows high-speed data processing in machine learning algorithms, which is necessary to support complex multistep in ML.
GPU vs CPU: Editing
Long rendering times during editing can block the creative flow. Since CPUs don’t support parallel processing, they can’t help speed up the process much. GPU parallel processing along with built-in AI capabilities like Tensor Cores or CUDA programming reduces rendering time.
CPU vs GPU: Overheat
CPU is small in size and generates less heat within computer systems. However, in the case of CPU and GPU combo (integrated graphics), the temperature may rise to dangerous levels if using mediocre-quality heatsinks and cooling fans. In contrast, GPUs are bulkier and perform more resource-intensive tasks than CPUs, generating excessive heat.
Many powerful graphics cards require additional cooling fans to dissipate heat effectively. Otherwise, it can cause damage to the GPU or other components of the computer system.
CPU vs GPU: Power Consumption
GPUs consume more power than CPUs because they have more cores and perform multithreading operations simultaneously. Because of the extensive power requirements, GPUs require high-quality Power Supply Units (PSUs) to sustain their operations.
CPU vs GPU: Limitation
The following section will highlight some limitations of CPUs and GPUs in enterprise applications.
APIs
API (Application Programming Interface) allows communication between computer programs. Data APIs work seamlessly with CPUs without limitations. While GPUs have graphics APIs designed for rendering graphics and other visual effects using Directx and OpenGL. In simpler terms, both CPU and GPUs are limited by their respective supported APIs, CPU can’t work with graphics APIs and GPUs with data APIs.
Parallel Processing
CPUs only work in serial processing, unlike GPUs, making them less adept at handling large numbers of identical tasks. In contrast, GPUs function via parallel processing allowing enterprises to use them for resource-intensive workloads like neural networks.
CPU vs GPU: Price
The price range of CPU processors is between $20 to $26,000. While GPUs range from $40 to $11,999. However, these price ranges can vary based on the model, form factor, IT brands, processing speeds, and more.
GPU vs CPU: Pros and Cons
Every computer hardware has its pros and cons. Let’s look into some limitations and drawbacks to help you understand CPU and GPU differences.
| Pros | Cons | |
| CPU | Control: Manages and coordinates system operations effectively. | Parallel Processing: Less efficient at parallel tasks compared to GPUs. |
| Control: Manages and coordinates system operations effectively. | Performance: Slower for specialized tasks like deep learning. | |
| Cost: Generally more cost-effective than high-end GPUs. | Graphics: Not optimized for graphics-intensive tasks. | |
| GPU | Parallel Processing: Highly efficient at parallel processing tasks. | Power Consumption: Generally consumes more power than CPUs. |
| Performance: Significantly accelerates tasks like deep learning. | Cost: High-performance GPUs can be expensive. |
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| Data Handling: Can handle large amounts of data simultaneously. | Complexity: Requires specialized programming knowledge (CUDA). |
GPU vs CPU: Which One to Choose?
CPU is a crucial component without which your system won’t function, while GPU is a secondary component used to boost performance. Regardless, both CPU and GPU are important in resource-intensive industries where complex and graphical operations are performed. That said, understand both CPU and GPU differences before making your purchase.
Computing Worlds offers new, used, and refurbished CPUs and GPUs for sale at the best price. Below are some best products from both devices selected for your needs.
Popular GPUs for Sale
Popular CPUs for Sale
You can request a free bulk quote on the required GPUs and CPUs.
CPUs vs GPUs: Future Proofing
The future is filled with advancements in AI technology. We can expect GPUs and CPUs used in machine learning models to train smaller and larger neural networks. GPUs have been the backbone of generative AI and since the announcement of NVIDIAs Blackwell AI GPU chips, the value of GPUs will grow exponentially.
CPUs on the other hand, may left behind in the AI race, but still, be crucial in managing and controlling entire computer system operations. Therefore, no matter how advanced GPUs become, they can never replace CPUs.
Frequently Ask Questions:
When to Upgrade CPU and GPU?
When you experience low performance (bottlenecks) during operations. Check the application system requirement and upgrade the GPU or CPU according.
What Affects Video Editing Speed CPU or GPU?
GPU affects the video editing process due to its multicore architecture and built-in AI features such as tensor cores.
Does Overclocking affect my CPU and GPU Performance?
Yes, overclocking increases the system performance, but increases power consumption and heat dissipation.
Is underclocking bad for my CPU and GPU Health?
No, underclocking if done correctly can increase CPU and GPU lifespan, reducing power consumption and heat dissipation.
CPU is commonly used in enterprises as the system can’t function without it. In contrast, GPUs are used in specialized conditions mostly in AI applications to boost system performance. Besides CPUs and GPUs other factors like SSD interface type, also impact the system performance such as SATA or NVMe. This marks the end of our debate between CPU vs. GPU.
When it comes to selecting a CPU processor there are many top reasons to get an AMD processor yet recently Intel processors also became a great option. Therefore, it’s important to evaluate your performance criteria and check the compatibility of each system component for the best CPUs and GPUs.
Stay connected to the Computing Worlds blog for more comparisons between computer hardware.
