CPU vs. GPU: Which One is Right for Your Workload?

CPU vs. GPU: Which One is Right for Your Workload?

These days, there's been an increase in using GPU (graphics processing unit) clouds because they are simpler to set up and manage than regular CPU (central processing unit) clouds. This is especially true for projects involving artificial intelligence (AI), machine learning (ML), or other industries requiring GPU power.

Unlike typical cloud services that need flexibility and compatibility among various parts like processors, memory, and network connections, GPU clouds mainly focus on one type of task. As a result, managing these systems is much less complicated.

Many organizations prefer the NVIDIA H100 model when choosing hardware for most modern applications requiring powerful computing, such as large language model (LLM) training and heavy ML/diffusion computations. Users usually only have to decide how many GPUs they require instead of worrying about other details. Although fast networking capabilities are important, spending too much money on this aspect is not a significant issue since networks tend to be cheaper than GPUs.

CPU vs GPU Performance Analysis

Undoubtedly, tasks such as ML and DL training model development or rendering high-resolution videos can be efficiently accomplished using an on-premise GPU server, which offers many features and amenities not typically found in GPU cloud servers. It is worth noting that while a GPU-powered data center suits rudimentary modeling and training needs, the precision of computation varies across different GPU models, with some providing double-precision and others single-precision values. Therefore, it is imperative to consider the level of precision needed for accurate modeling and choose between the two accordingly.

Organizations that struggle with managing on-premise servers can greatly benefit from GPU cloud servers. By choosing cloud infrastructure with GPU-based processing, these entities gain the advantage of optimal performance, precise memory allocation, and robust disk capabilities. In contrast, deploying on-premises servers without an experienced IT team can be daunting and may result in suboptimal outcomes. Therefore, organizations must consider GPU cloud servers as a reliable and efficient alternative.

The operational reliability of GPU cloud servers surpasses that of on-premise servers in various ways. From network disruptions and equipment failures to power outages and storage constraints, on-premise servers are prone to numerous operational issues that can significantly impact their performance. On the other hand, cloud-based solutions offer greater stability and resilience as cloud service providers (CSPs) manage these servers promptly and efficiently, ensuring uninterrupted service. Therefore, opting for a cloud-based GPU solution is the best decision as it eliminates the burden of resolving the issues associated with on-premise server maintenance.

Why GPUs Perform Better Tasks

Parallel Processing Capabilities

GPUs are designed to handle multiple operations simultaneously. This parallel processing is vital for AI tasks, which often involve complex mathematical computations. Unlike traditional CPUs, which process tasks sequentially, GPUs can execute thousands of threads simultaneously, drastically reducing computation time for AI algorithms.

Handling Large Datasets

AI and machine learning often require the processing of vast amounts of data. GPUs excel in this area due to their high-bandwidth memory and large number of cores, allowing faster and more efficient data handling. This capability is crucial in training deep learning models, where large datasets are the norm.

Specialized for Computation-Intensive Tasks

GPUs are optimized for computationally intensive tasks. They have a large number of arithmetic logic units (ALUs), enabling them to perform more calculations per second. This makes them particularly effective for the matrix and vector operations common in machine learning algorithms.

Accelerating Deep Learning Frameworks

Most deep learning frameworks are optimized for GPUs. Frameworks like TensorFlow, PyTorch, and CUDA provide libraries and tools specifically designed to leverage GPU architecture, further enhancing their performance in AI tasks.

Image and Speech Recognition

In applications such as image and speech recognition, GPUs demonstrate remarkable efficiency. Their ability to quickly process and analyze pixels in images or audio data in speech makes them indispensable for these applications.

Case Studies and Examples

Case studies and examples are powerful tools for grasping how various hardware setups affect data performance. Here are a few examples:

  • NVIDIA Tesla V100: A recent study conducted by the University of Bristol has demonstrated that using NVIDIA Tesla V100 GPUs can significantly accelerate deep learning workloads for drug discovery. According to the research, GPUs boost computations by an impressive 15-20x compared to CPUs, slashing the time required to train complex models from days to just a few hours.

  • AWS EC2 P3 Instances: The AWS EC2 P3 instances are indisputably powered by NVIDIA V100 GPUs and are unequivocally designed for machine learning and high-performance computing workloads. A decisive case study by NVIDIA unequivocally demonstrated that a company could drastically reduce the time required for image classification from 1.5 hours to 3 minutes using AWS EC2 P3 instances.

  • Intel Xeon Scalable Processors: Intel conducted a case study that proved how upgrading to Intel Xeon Scalable processors can significantly reduce the time required to process complex financial models. These processors can dynamically increase clock speed by leveraging advanced features like Intel Turbo Boost Technology to enhance performance. As a result, the time taken to complete such tasks can be reduced from 24 hours to just 10 minutes.

Cost Analysis

It is important to note that GPU's efficiency and power performance are highly commendable despite the tradeoffs in cost and performance. One must consider the cost dynamics and operational efficiencies between CPU and GPU servers and hyperscale cloud providers like Google, Amazon, and Microsoft play a significant role in optimizing data center operations. In the case of CPU servers, the costs associated with hosting are relatively similar to the capital costs.

However, in the case of GPU servers, the hosting costs are much lower compared to the capital costs, indicating a significant disparity that should not be overlooked.

This difference lies in CPU and GPU server architectures and their respective costs. CPU servers typically have lower capital costs but higher hosting costs, while GPU servers exhibit the opposite trend, with higher capital costs but lower hosting costs.

Cost distribution plays a vital role in the existence of third-party cloud providers such as Amazon, Google, and Microsoft. These providers operate at a massive scale and have optimized their data center operations to reduce hosting costs significantly. One of the essential metrics for this optimization is the Power Usage Effectiveness (PUE), which measures the data center's efficiency by comparing the total energy consumed with the energy delivered to computing equipment. The lower the PUE, the more efficient the data center. Hyperscale cloud providers are experts in optimizing PUE through various means, such as efficient cooling and power delivery systems. This results in PUE values approaching as close to 1 as possible, indicating highly efficient data center operations.

When choosing between general-purpose chips and GPU servers, the decision heavily relies on factors such as the workload nature, the requirement for parallel processing, cost considerations, and overall performance needs. It's important to carefully analyze these factors, as each type of server has its own advantages and applications. However, it's worth noting that GPUs are generally the preferred choice for any high-tech-related use case due to their exceptional ability to deliver standalone performance.

CPU vs. GPU: Pros and Cons

CPU Advantages and Limitations

CPUs have several distinct advantages for modern computing tasks:

  • Versatility: A central processing unit (CPU) is a multipurpose processor capable of executing various tasks and switching between different activities efficiently.

  • Speed advantage: CPUs generally outperform graphics processing units (GPUs) regarding data processing in random access memory (RAM), input/output (I/O) operations, and operating system management due to their optimized design for these functions.

  • Precise calculations: CPUs offer better accuracy for arithmetic operations than GPUs, making them more suitable for applications requiring high computational precision.

  • Cache memory advantages: CPUs with larger onboard cache memory excel at handling extensive collections of sequential commands or instructions effectively.

  • Hardware interoperability: Unlike GPUs, CPUs ensure broad compatibility across diverse motherboards and system configurations since they do not necessitate specific hardware accommodations.

CPUs have the following disadvantages compared to GPUs:

  • Limited parallelism: CPUs encounter challenges in managing tasks demanding vast quantities of uniform procedures owing to restricted concurrency capabilities.

  • Slower advancements: Given their maturity as established technologies nearing their growth ceiling, CPUs face constraints in innovation compared to GPUs, which boast considerable room for improvement.

  • Compatibility issues: With several distinct CPU variants, such as x86 and ARM architectures available, certain software might lack compatibility across varying platforms. This highlights the importance of selecting appropriate hardware based on application requirements.

GPU Advantages and Limitations

The unique advantages of GPUs include:

  • Enhanced data flow: Graphics processing units (GPUs) can execute similar operations simultaneously on numerous data elements, enabling rapid processing of substantial datasets beyond the reach of CPUs.

  • Extensive parallelization capacity: Equipped with abundant computing cores, GPUs facilitate highly parallelizable mathematical processes, such as matrix multiplication, thereby surpassing CPUs in performance efficiency.

  • Tailored functionality: Ideally suited for niche workloads, GPUs deliver remarkable speedups for targeted domains like deep learning, large-scale data analysis, and genomics sequencing.

Disadvantages of GPUs compared to CPUs include:

  • Singular focus: Although GPUs demonstrate exceptional proficiency in single, wide-scale tasks, they fall short in accomplishing comprehensive computational duties typically handled by CPUs.

  • Cost considerations: Single GPUs tend to be pricier than individual CPUs, while expansive, specialized GPU arrays could cost hundreds of thousands of dollars, presenting budgetary concerns.

  • Intricate procedure management: Despite their raw power, GPUs may falter when dealing with intricate workflows involving conditional statements, sequential steps, or sophisticated control structures prevalent in typical computer programs.

Which one is better: CPU vs GPU performance

GPUs are essential for deep learning and Artificial Intelligence (AI) tasks. Their unparalleled ability to perform multiple calculations simultaneously gives them an unbeatable advantage in image and video processing tasks. In deep learning, using CPUs instead of GPUs could be a grave mistake, as GPUs can perform the necessary matrix operations and other calculations much faster.

The use of GPUs has been the key factor in the recent advances in deep learning, as they have made it possible to train larger and more complex models in a reasonable amount of time. Once a model has been trained, deploying it on a CPU or GPU is feasible, depending on the application's requirements. However, when running deep learning models on large datasets or in real-time applications, using multiple GPUs or a combination of CPUs and GPUs is crucial.


In conclusion, GPUs have emerged as a superior option over CPUs for specific compute-intensive tasks, particularly those involving artificial intelligence, machine learning, and high-resolution rendering. The simplicity of setting up and managing GPU cloud servers and their ability to handle multiple operations simultaneously make them ideal for tackling large datasets and computationally intensive tasks. Organizations struggling with on-premise server management can significantly benefit from GPU cloud servers, which offer optimal performance, precise memory allocation, and robust disk capabilities.

Moreover, GPU cloud servers prove more operationally reliable than on-premise servers, given their resistance to network disruptions, equipment failures, power outages, and storage constraints. While both options have pros and cons, GPUs' enhanced data flow, extensive parallelization capacity, and tailored functionality make them the go-to choice for high-tech-related use cases. Ultimately, carefully considering workload nature, parallel processing requirements, cost implications, and overall performance needs will guide businesses toward selecting the right server for their organization.