CPU vs. GPU for Machine Learning

While CPUs can process many general tasks in a fast, sequential manner, GPUs use parallel computing to break down massively complex problems into multiple smaller simultaneous calculations. This makes them ideal for handling the massively distributed computational processes required for machine learning.

In this article, we’ll compare the differences between a CPU and a GPU, as well as the applications for each with machine learning, neural networks, and deep learning.

What Is a CPU?

A central processing unit, or CPU, is a processor that processes the basic instructions of a computer, such as arithmetic, logical functions, and I/O operations. It’s typically a small but powerful chip integrated into the computer’s motherboard.

A CPU is considered the computer’s brain because it interprets and executes most of the computer’s hardware and software instructions.

Standard components of a CPU include one or more cores, cache, memory management unit (MMU), and the CPU clock and control unit. These all work together to enable the computer to run multiple applications at the same time.

The core is the central architecture of the CPU where all the computation and logic occur. Today’s advanced CPUs feature Intel’s 4th and 5th Gen Xeon processors with integrated AI accelerators (TMUL) specifically designed for inference workloads. These modern processors deliver between 30-50 tokens per second on optimized models, sufficient for applications like chatbots and document summarization.

What Is a GPU?

A GPU, or graphics processing unit, is a computer processor that uses accelerated calculations to render intensive high-resolution images and graphics. While originally designed for rendering 2D and 3D images, videos, and animations on a computer, today’s GPUs are used in applications far beyond graphics processing, including big analytics and machine learning.

The GPU landscape has evolved dramatically by 2025, with NVIDIA’s H200 Tensor Core GPUs featuring 141GB HBM3 memory and 4.8 TB/s memory bandwidth leading the market. AMD’s MI300X and Intel’s Data Center GPU Max Series have also made significant advancements, creating a competitive ecosystem for AI acceleration.

GPUs function similarly to CPUs and contain similar components (e.g., cores, memory, etc). They can be integrated into the CPU or they can be discrete (i.e., separate from the CPU with its own RAM).

GPUs use parallel processing, dividing tasks into smaller subtasks that are distributed among a vast number of processor cores in the GPU. This results in faster processing of specialized computing tasks.

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CPU vs. GPU: What’s the Difference?

The fundamental difference between GPUs and CPUs is that CPUs are ideal for performing sequential tasks quickly, while GPUs use parallel processing to compute tasks simultaneously with greater speed and efficiency.

CPUs are general-purpose processors that can handle almost any type of calculation. They can allocate a lot of power to multitask between several sets of linear instructions to execute those instructions faster.

While CPUs can perform sequential tasks on complex computations quickly and efficiently, they are less efficient at parallel processing across a wide range of tasks. Today’s CPUs offer memory bandwidth around 50GB/s, while top GPUs now reach up to 7.8 TB/s – a critical factor for data-intensive AI workloads.

GPUs are excellent at handling specialized computations and can have thousands of cores that can run operations in parallel on multiple data points. By batching instructions and pushing vast amounts of data at high volumes, they can speed up workloads beyond the capabilities of a CPU.

Training deep neural networks on GPUs can be over 10 times faster than on CPUs with equivalent costs. However, CPU architectures have improved dramatically for certain AI workloads—particularly inference—making them a viable option for specific deployment scenarios.

In this way, GPUs provide massive acceleration for specialized tasks such as machine learning, data analytics, and other artificial intelligence (AI) applications. The dramatically increased VRAM capacities, with top ML GPUs now offering 80-188GB of memory, enable processing of significantly larger models.

How Does a GPU Work?

While CPUs typically have fewer cores that run at high speeds, GPUs have many processing cores that operate at low speeds. When given a task, a GPU will divide it into thousands of smaller subtasks and process them concurrently, instead of serially.

Tensor Cores in modern GPUs significantly enhance matrix multiplication operations that are foundational to neural network training. These specialized cores can execute mixed-precision calculations that dramatically accelerate AI training while maintaining accuracy.

In graphics rendering, GPUs handle complex mathematical and geometric calculations to create realistic visual effects and imagery. Instructions must be carried out simultaneously to draw and redraw images hundreds of times per second to create a smooth visual experience.

GPUs also perform pixel processing, a complex process that requires phenomenal amounts of processing power to render multiple layers and create the intricate textures necessary for realistic graphics.

It is this high level of processing power that makes GPUs suitable for machine learning, AI, and other tasks that require hundreds or thousands of complex computations. Teams can increase compute capacity with high-performance computing clusters by adding multiple GPUs per node that can divide tasks into thousands of smaller subtasks and process them all at the same time.

Energy efficiency has also become a critical consideration for large-scale AI deployments, with 2025 GPUs achieving approximately 25% reduction in energy requirements compared to 2024 models, significantly reducing operational costs for AI infrastructure.

CPU vs. GPU for Machine Learning

Machine learning is a form of artificial intelligence that uses algorithms and historical data to identify patterns and predict outcomes with little to no human intervention. Machine learning requires the input of large continuous data sets to improve the accuracy of the algorithm.

While CPUs aren’t considered as efficient for data-intensive machine learning processes, they are still a cost-effective option when using a GPU isn’t ideal. High-end GPUs like NVIDIA’s H100 can cost upwards of $25,000 per card, making cost-efficiency a critical consideration in infrastructure planning.

Such use cases include machine learning algorithms, such as time series data, that don’t require parallel computing, as well as recommendation systems for training that need lots of memory for embedding layers. Some algorithms are also optimized to use CPUs over GPUs.

The more data, the better and faster a machine learning algorithm can learn. The technology in GPUs has advanced beyond processing high-performance graphics to use cases that require high-speed data processing and massively parallel computations. As a result, GPUs provide the parallel processing necessary to support the complex multistep processes involved in machine learning.

Benchmark data from 2025 shows training speed improvements of 40-60% over 2024 models and memory efficiency improvements with reduced latency by up to 35%, demonstrating the continued acceleration of GPU capabilities for machine learning tasks.

CPU vs. GPU for Neural Networks

Neural networks learn from massive amounts of data in an attempt to simulate the behavior of the human brain. During the training phase, a neural network scans data for input and compares it against standard data so that it can form predictions and forecasts.

Because neural networks work primarily with massive data sets, training time can increase as the data set grows. While it’s possible to train smaller-scale neural networks using CPUs, CPUs become less efficient at processing these large volumes of data, causing training time to increase as more layers and parameters are added.

Neural networks form the basis of deep learning (a neural network with three or more layers) and are designed to run in parallel, with each task running independently of the other. This makes GPUs more suitable for processing the enormous data sets and complex mathematical data used to train neural networks.

S3-over-RDMA technology has emerged as a critical advancement for neural network training, accelerating AI data transfer by increasing throughput and reducing CPU utilization. This technology enables faster data ingestion for training large-scale models and has become a standard feature in enterprise AI infrastructure.

CPU vs. GPU for Deep Learning

A deep learning model is a neural network with three or more layers. Deep learning models have highly flexible architectures that allow them to learn directly from raw data. Training deep learning networks with large data sets can increase their predictive accuracy.

CPUs are less efficient than GPUs for deep learning because they process tasks in order one at a time. As more data points are used for input and forecasting, it becomes more difficult for a CPU to manage all of the associated tasks.

Deep learning requires a great deal of speed and high performance and models learn more quickly when all operations are processed at once. Because they have thousands of cores, GPUs are optimized for training deep learning models and can process multiple parallel tasks up to three times faster than a CPU.

Enterprise case studies, like CoreWeave’s partnership with Pure Storage, demonstrate how GPU-accelerated infrastructure delivers significant performance improvements for deep learning applications. Meanwhile, CPU-based inference has found its place in scenarios where low latency is crucial for real-time applications with smaller models.

Power Machine Learning with Next-gen AI Infrastructure

GPUs play an important role in the development of today’s machine learning applications. When choosing a GPU for your machine learning applications, there are several manufacturers to choose from, but NVIDIA, a pioneer and leader in GPU hardware and software (CUDA), leads the way.

AIRI//S™ is modern AI infrastructure architected by Pure Storage® and NVIDIA and powered by the latest NVIDIA DGX systems and Pure Storage FlashBlade//S™. The solution has been updated with the newest capabilities, including support for NVIDIA’s latest GPU technologies and Pure’s most advanced storage systems.

Pure Storage’s AI strategy has evolved significantly, with the company now positioned as an essential component of the AI infrastructure stack. As a certified NVIDIA Cloud Partner, Pure enables AI cloud providers to reduce the time and cost of deploying AI solutions at scale.

AIRI//S is an out-of-the-box AI solution that simplifies your AI deployment to deliver simple, fast, next-generation, future-proof infrastructure to meet your AI demands at any scale. It’s now complemented by FlashBlade//EXA, positioned as the industry’s most advanced scale-out storage solution for AI and high-performance computing.

Pure Storage addresses the storage bottlenecks that limit GPU efficiency in large-scale AI workloads. The company’s integration of S3-over-RDMA into FlashBlade accelerates AI training and inference outcomes by enabling high-throughput data ingest with minimal CPU overhead.Strategic partnerships with industry leaders like NVIDIA, CoreWeave, and Cisco through FlashStack help businesses streamline AI workloads while providing the always-on quality of service (QoS) required for high-level read/write performance guarantees in production AI deployments.