Traditional computing relied heavily on the Central Processing Unit (CPU), which remains the brain of most devices. However, AI applications require massive amounts of data to be processed simultaneously, which has created a demand for specialised processors. 

Instead of relying on a single type of chip, modern systems often combine multiple processors, with each handling the tasks it performs best.

CPU: The all-purpose processor

The CPU, or Central Processing Unit, is the general-purpose processor found in every computer, smartphone, and server. 

It is designed to handle a wide variety of tasks, including operating systems, applications, web browsing, and business software. CPUs excel at sequential processing, where instructions are executed one after another with high accuracy and flexibility. 

Key strengths of CPUs include:

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  Managing overall system operations
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  Running general software applications
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  Handling complex decision-making tasks
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  Coordinating other processors in a system

While CPUs can run AI models, they are generally not the fastest or most efficient option for large-scale AI workloads.

GPU: The parallel processing powerhouse

Graphics Processing Units, or GPUs, were originally developed to render graphics for gaming and visual applications. However, their ability to perform thousands of calculations simultaneously made them ideal for AI and machine learning. 

Unlike CPUs, which focus on a few powerful processing cores, GPUs contain thousands of smaller cores that can process large volumes of data parallelly. 

This makes GPUs particularly effective for:

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  Training large AI models
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  High-performance computing
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  Image and video processing
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  Scientific simulations

TPU: Google’s AI specialist

The Tensor Processing Unit, or TPU, is a specialised AI accelerator developed by Google. Unlike GPUs, which serve multiple purposes, TPUs are specifically designed for machine learning operations involving neural networks and tensor calculations. This focused design allows them to deliver high efficiency for AI training and inference workloads. 

TPUs are primarily used within Google’s cloud ecosystem and power many of the company’s AI services. Recent generations have been built to support increasingly large AI models while improving performance and energy efficiency.

NPU: Bringing AI directly to devices

The newest member of the processor family is the NPU, or Neural Processing Unit. NPUs are designed specifically for AI tasks and are increasingly appearing in smartphones, personal computers, bringing AI capabilities directly to devices instead of sending every task to remote data centres. 

Their primary advantage is efficiency, as they can perform AI calculations while consuming significantly less power than CPUs or GPUs. 

Common NPU-powered functions include:

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  Real-time language translation
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  AI photo and video enhancements
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  Voice recognition
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  On-device generative AI features

Since AI processing can happen locally, NPUs can improve privacy, reduce latency, and lower dependence on cloud services.

Where hyperscalers fit in

The rise of AI has also increased the importance of hyperscalers, cloud companies that operate enormous data centres capable of scaling computing resources globally. 

Major hyperscalers such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud rely on a mix of CPUs, GPUs, TPUs, and other specialised accelerators to deliver cloud computing and AI services at scale. 

As demand for AI grows, hyperscalers are investing in their own specialised chips to improve performance and manage costs. Google’s TPU is a prominent example of how cloud giants are building custom hardware to strengthen their AI capabilities.