AI Basics: CPU, GPU and TPU, Role, Importance, Latest News

A CPU (Central Processing Unit) handles general-purpose computations and task scheduling but is less optimized for large-scale AI training.

A GPU (Graphics Processing Unit) excels at parallel processing, making it ideal for training deep learning models.

A TPU (Tensor Processing Unit) is an AI-specific processor developed by Google designed to accelerate machine learning workloads.

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AI Basics: CPU, GPU and TPU Latest News

How TPU is Different from CPU and GPU

Processing units are the core hardware components that act as the brain of a computer. They perform tasks such as calculations, processing images, and transmitting data, similar to how the human brain executes complex tasks.

The CPU is a general-purpose processor that was developed in the 1950s and can handle a wide variety of tasks.
It functions like a conductor in an orchestra, coordinating the operations of all other computer parts like GPUs, disk drives, and memory units.
A CPU contains cores — individual units that execute instructions. Early CPUs had only one core, but modern CPUs may contain 2 to 16 cores.
Each core can handle one task at a time, so a CPU’s multitasking capacity depends on the number of cores.
For everyday users, 2 to 8 cores are usually sufficient, and CPUs are so efficient that users rarely notice that tasks are completed sequentially, not simultaneously.

A GPU is a specialised processor designed to perform many tasks simultaneously, using a technique called parallel processing.
Unlike CPUs, which process tasks sequentially, GPUs break down complex tasks into thousands or millions of smaller problems, solving them in parallel.
Modern GPUs contain thousands of cores, making them far more suitable for intensive computational tasks.
Initially developed for rendering graphics in gaming and animation, GPUs are now widely used in machine learning and artificial intelligence.
GPUs have evolved into general-purpose parallel processors, making them a key tool in running AI models and handling large data operations.
However, GPUs have not replaced CPUs, because certain operations are better handled sequentially, which is the strength of CPUs.

A TPU is also a type of ASIC (Application-Specific Integrated Circuit), meaning it is built for a specific function — in this case, AI tasks.
First introduced by Google in 2015, TPUs are specially designed hardware units built from the ground up to handle machine learning operations.
TPUs focus on processing tensors — the multidimensional data arrays used in AI model computations.
They are optimised to run neural networks efficiently, enabling faster training and execution of AI models than GPUs or CPUs.
For example, training an AI model that may take weeks on a GPU can often be completed in hours using a TPU.
TPUs are used at the core of Google’s major AI services, such as Search, YouTube, and DeepMind’s large language models, illustrating their real-world application in high-scale AI infrastructure.