NPU Vs GPU Vs CPU - Know The Key Differences Today!

Key Takeaways

• The GPU (Graphics Processing Unit), traditionally built for graphics rendering, has now evolved for parallel computing and intensive workloads. 

• GPU is ideal for gaming, training AI models, simulations, and content creation.

• The NPU (Neural Processing Unit) is designed and optimized for AI workloads. It is power-efficient and reduces latency.

• NPUs power on-device AI features such as face recognition, speech recognition, and language translations.

• The CPU (Central Processing Unit), is the core processor that manages and executes all the tasks in a laptop.

• The CPU handles browsing, running software applications, working on MS Office applications, and more.

• While both GPUs and NPUs excel at parallel processing, their roles differ. GPUs train AI models and handle high-performance and cloud-based AI workloads, whereas NPUs manage on-device AI features.

• Step into the next generation of computing with ASUS Zenbook 14 (UX3407) and Zenbook S14 (UX5406).

Traditionally, CPU specifications were the primary focus when purchasing a computer. With advancements in gaming, graphics, and content creation, GPUs soon gained importance. Now, with the rapid rise of AI-driven applications, NPUs are becoming increasingly prominent. If you are moving towards smarter computing experiences, understanding NPU vs GPU vs CPU is essential.

Since NPU and GPU are often the most commonly confused, we will explore NPU vs GPU for better clarity. We will also delve into NPU vs CPU and understand how these two components differ in performance and use cases. 

Before we discuss the difference between GPU, CPU, and NPU, let us understand what each of them means.

What is a GPU?

The full form of the GPU is the Graphics Processing Unit. It is an electronic microprocessor originally designed to render graphics. A GPU in a laptop has thousands of cores that can perform one operation across large volumes of data, demonstrating parallel processing. When you play games, edit photos or videos, or stream content, the GPU handles the graphics-related tasks. 

With the advancement in technology, GPUs have evolved to perform complex and advanced computing tasks, including Artificial Intelligence (AI) and Machine Learning (ML).

GPU use cases

• Gaming

• Content creation

• Simulation

• AI model training

• Machine learning and research

Key features

• Built with thousands of cores for parallel processing.

• VRAM (Video Random Access Memory) is a dedicated memory for quick access to data.

• Higher power consumption for high-performance computing needs.

• Available as integrated or discrete GPUs. 

What is an NPU?

The full form of NPU is Neural Processing Unit. It is a microprocessor designed and optimized specifically for AI workloads like recognising speech and facial data. NPUs simulate or mimic how the human brain processes information and responds intelligently to images, voice, audio, and video.

In simple terms, an NPU receives input in the form of text, video, or audio and processes it using trained patterns to produce an output.

NPUs are optimized for AI workloads like speech recognition, and whenever a supported AI workload is detected in the workflow, an NPU will intelligently take over the task from the CPU, freeing up the CPU for other processes.

NPU use cases

• Face recognition

• Speech recognition

• Real-time language translation

Key features

• Specialized framework that enables parallel processing for high-speed performance. 

• Use simplified or low-precision arithmetic to reduce latency and enable faster responses. 

• Highly power-efficient for smaller AI workloads.

• On-chip memory that allows quick access to data. 

What is a CPU?

The full form of the CPU is the Central Processing Unit. It is the central control system and the core processor that manages and executes all tasks in your computer. 

For example, when you start working and open multiple applications, the CPU in the laptop processes your commands and manages system resources to ensure smooth overall functioning.

CPU use cases

• Browsing the internet

• Working on MS Office applications (MS Word, MS Excel, etc.)

• Streaming videos

• Running software applications

Key features

• Sequential processing, which means handling instructions one after another.

• Designed for general-purpose computing.

• Can manage a wide range of everyday and complex applications.

NPU vs CPU vs GPU: Key differences

How do these processing units work together?

In a laptop equipped with a CPU, NPU, and GPU, tasks are allotted based on their purpose. AI-related tasks are managed by the GPU and NPU, which frees up the CPU to focus on core system operation and overall performance. 

When each processor is designated with specific tasks, the overall system efficiency improves. The GPU works on heavy computing workloads and rendering graphics, while the NPU manages AI workloads in the background with lower power consumption and minimal latency. 

This well-balanced workload distribution ensures smoother performance, better multitasking, and improved battery life.

GPU vs NPU: Which is better?

With the rapid evolution of AI, this has become a common question. Why do modern AI PCs use both an NPU and a GPU? Should you get a laptop with NPU?

Let’s explore. 

Well, NPU and GPU are designed for two different purposes.

Role of NPU for smart, on-device AI

NPUs handle continuous, real-time AI workloads that make everyday computing smarter and more efficient. If you want handy built-in AI features and want a laptop that can offer good battery life despite a lot of AI workloads, then a laptop with an NPU may be ideal for you.

NPUs enable a wide range of AI-powered features, including: 

• Video enhancements, such as background blur and eye contact correction for video conferencing

• Audio enhancements like noise cancellation

• Integration with third-party programs for smarter workflows, like with certain image and video editing software.

• Improved voice recognition and language translation

As NPUs are designed to process these features efficiently on the device, you can expect superior performance, lower power consumption, reduced internet dependency, and improved security.

Role of GPU in high-performance AI workloads 

GPUs handle high-performance workloads that need intense parallel processing power, right on your device. This includes tasks like AI model training and Machine Learning. .                                         

In addition to AAA gaming, a GPU with higher specifications becomes essential for heavier workloads, such as:

• Training AI models

• Content creation

• Machine Learning

Combine the strengths of NPU and GPU for future computing needs

AI usage today is extended to both on-device features and cloud-based workloads. Laptops equipped with both an NPU and a GPU deliver faster AI responses, better battery efficiency, and improved security.

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To summarize

The NPU, GPU, and CPU each play distinct roles in a modern computer. The CPU is the core processor that manages system operations and allocates resources. And the NPU and GPU handle different kinds of AI workloads. 

By offloading AI tasks to the NPU, the CPU and GPU are freed up for other tasks. This improves overall system efficiency and performance.

Explore ASUS AI PCs to experience future-ready performance for evolving AI-driven applications.

FAQs

Q1. How is NPU different from GPU and CPU?

An NPU (Neural Processing Unit) is designed specifically to handle on-device AI workloads. This includes speech recognition, face recognition, and similar other real-time AI enhancements. These tasks are performed on the device and do not rely much on cloud-based environments and internet connectivity.

A GPU (Graphics Processing Unit), originally designed for graphics processing, is now widely used for high-intensity computing workloads, including AI model training and Machine Learning.

Both NPUs and GPUs excel at parallel processing, but they are optimized for different purposes.

A CPU (Central Processing Unit) is the core processor that manages all the tasks running in a computer. It executes instructions one at a time and allocates system resources efficiently. 

Q2. Are GPU TOPS better than NPU TOPS?

A GPU and an NPU are designed for different purposes. Hence, they cannot be compared. 

A GPU may deliver a higher TOPS value for high-intensity parallel processing needs, such as AI model training. On the other hand, an NPU offers a lower TOPS value but superior battery efficiency for smaller, less complex AI workloads. They can run everyday AI tasks like speech recognition and face recognition without draining your battery. 

Q3. Will NPUs replace GPUs?

No, NPUs are not designed to replace GPUs. GPUs are designed for graphics rendering and intense workloads like AI model training and Machine Learning. An NPU, on the other hand, complements a GPU by handling AI workloads on the device, such as speech recognition, language translation, and face recognition.