CPU vs GPU vs TPU – Simple Explanation (Interview-Ready)

1️⃣ What is a CPU?

CPU (Central Processing Unit)

The CPU is the brain of the computer.

• Handles general-purpose tasks

• Very flexible

• Excellent at decision making and control logic

• Has few powerful cores

Simple analogy

🧠 CPU = A very smart manager handling one task at a time very efficiently.

Example

• Running an operating system

• Executing Java / Python programs

• Handling API requests in a backend service

Interview line

A CPU is designed for general-purpose computing with strong single-thread performance.

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2️⃣ What is a GPU?

GPU (Graphics Processing Unit)

The GPU is designed to do many similar calculations at the same time.

• Thousands of small cores

• Best for parallel processing

• Originally built for graphics

• Widely used for ML & AI

Simple analogy

🎨 GPU = Thousands of workers doing the same task in parallel.

Example

• Image rendering

• Video processing

• Training machine learning models

• Matrix multiplication

Interview line

GPUs excel at massively parallel computations.

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3️⃣ What is a TPU?

TPU (Tensor Processing Unit)

A TPU is a custom chip designed specifically for machine learning.

• Built by Google

• Optimized for tensor operations

• Extremely fast for AI workloads

• Less flexible than CPU/GPU

Simple analogy

🤖 TPU = A factory machine built for one job only: AI math.

Example

• Training large neural networks

• Running deep learning inference at scale

• Google Search, Translate, Photos

Interview line

TPUs are specialized accelerators optimized for machine learning workloads.

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4️⃣ Side-by-Side Comparison

Feature	CPU	GPU	TPU
Purpose	General computing	Parallel computing	AI/ML acceleration
Cores	Few (4–64)	Thousands	Matrix units
Flexibility	Very high	Medium	Low
Best at	Control logic	Parallel math	Tensor operations
Used for	OS, APIs, apps	ML, graphics	Deep learning
Cost	Moderate	High	Cloud-based

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5️⃣ Simple Real-World Example (Restaurant)

CPU

👨‍🍳 One expert chef preparing different dishes one by one.

GPU

👩‍🍳👩‍🍳👩‍🍳 Many chefs cooking the same dish in parallel.

TPU

🏭 A machine built only to mass-produce one specific dish very fast.

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6️⃣ When to Use What?

Use CPU when:

• Business logic

• APIs

• Databases

• Control flow

Use GPU when:

• Image/video processing

• ML training

• Parallel computations

Use TPU when:

• Large-scale AI training

• Deep learning inference

• Cloud ML workloads

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7️⃣ Interview One-Liners (Very Important)

• CPU:

  CPU is best for general-purpose tasks and decision-heavy logic.

• GPU:

  GPU is optimized for massively parallel workloads like ML and graphics.

• TPU:

  TPU is a specialized accelerator designed for high-performance machine learning tasks.

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8️⃣ Final Summary (Memorize This)

CPUs are flexible and smart, GPUs are massively parallel, and TPUs are specialized for AI.