If servers are viewed as “super factories” in the field of data processing, then GPU servers are suited for intricate and complex data processing, while CPU servers excel at efficiently executing large-scale standardized tasks. Among them, certain “production lines” specialize in executing large-scale, standardized production tasks, enabling the efficient output of numerous data products; whereas other “production lines” focus more on precise and complex processes, ensuring each data product achieves exceptional quality. So, within this vast “factory” of data processing, how do these two types of “production lines” operate? And what distinctly different “products” do they yield?

Hardware Architecture

CPU Server Architecture
CPU servers excel at sequential execution and complex logic control. Their design focuses more on sequential execution and intricate logic control. A key characteristic is their relatively low core count. For instance, Intel Xeon Scalable processors typically range from 4 to 64 cores, such as the Intel Xeon Platinum 8380 processor which boasts 40 cores. These cores all possess robust capabilities for complex computations and logical processing, enabling CPU servers to efficiently handle single or a limited number of complex tasks, such as operating system scheduling and database transaction processing.

GPU Server Architecture
In contrast, GPU servers are equipped with a massive number of parallel computing cores. They are built for large-scale parallel computation and are outfitted with numerous computing cores. For example, NVIDIA’s A100 GPU, based on the advanced Ampere architecture, contains up to 8240 CUDA cores. While these cores are simpler individually, they are exceptionally well-suited for executing vast numbers of identical computational tasks, such as matrix operations and vector calculations.

Computational Capabilities

CPU Computational Capabilities
CPU servers demonstrate exceptional performance when handling tasks involving complex logic and control. Whether it’s database queries, file system management, or other scenarios requiring extensive data retrieval, sorting, and processing, CPUs are adept at managing complex logical tasks. However, when faced with large-scale parallel computing challenges, such as deep learning training tasks, the limitations of CPUs become apparent. Their core count restricts computational speed, particularly for tasks like image recognition or deep learning training that require massive data parallelism. CPUs often require significantly longer time to complete such computations.

GPU Computational Capabilities
Conversely, GPUs excel in parallel processing of large-scale data. GPU servers stand out due to their superior parallel computing power. When dealing with large-scale data-parallel tasks, GPUs can fully leverage their numerous cores to perform calculations on vast amounts of data simultaneously, thereby dramatically improving computational efficiency. Taking deep learning training as an example, when training a large image classification model, a GPU can process thousands of image data points in parallel, potentially reducing the required time by factors of several to tens compared to a CPU.

Application Scenarios

CPU Server Application Scenarios
CPU servers are suitable for enterprise business processing, including Enterprise Resource Planning systems like SAP ERP and Customer Relationship Management systems like Salesforce CRM. These systems require handling complex business logic and customer data. Simultaneously, CPU servers are the primary platform for relational databases such as Oracle and MySQL, managing transactions, query optimization, and data storage operations. Furthermore, CPU servers play a critical role in network and file services, such as Apache Web Server and file sharing services on Windows Server.

GPU Server Application Scenarios
On the other hand, GPU servers are particularly important in areas like deep learning, scientific computing, and graphics rendering. In fields such as speech recognition and image recognition, GPU servers accelerate model training and data processing. They are also widely used in scientific research, especially in computational fluid dynamics and molecular dynamics simulations. GPU servers can significantly enhance research efficiency in areas like weather forecasting, climate modeling, and drug discovery.

Power Consumption and Heat Dissipation

CPU Server Power Consumption and Heat Dissipation
Due to the relatively lower number of cores and the lower power consumption per core, CPU servers generally have lower energy demands. Air cooling systems are typically sufficient for heat dissipation. For instance, the Intel Xeon Silver 4310 processor has a Thermal Design Power of only 85W.

GPU Server Power Consumption and Heat Dissipation
In comparison, GPU servers require liquid cooling systems due to their high energy consumption. Because GPUs contain thousands of computing cores, they generate substantial heat during operation, with typical TDP values reaching up to 132W for a module, and systems housing multiple GPUs requiring significantly higher power delivery (server power supplies in the kilowatt range are common). To ensure stable GPU operation, more efficient cooling methods, such as liquid cooling systems, are essential, and the corresponding server power supply units must also deliver higher wattage.

In summary, GPU servers and CPU servers exhibit significant differences across multiple aspects, including hardware architecture, computational capabilities, application scenarios, as well as power consumption and heat dissipation requirements.