Chips and Semiconductors and chips for beginners : From Basics to AI Applications Before diving into the details,

Introduction

Understanding the Difference Between Chips and Semiconductors

From Basics to AI Applications

Before diving into the details, here’s what you need to know: Semiconductors are special materials that can control electricity.

At the same time, chips are manufactured devices made from these materials containing miniaturized electronic circuits.

Modern AI applications like OpenAI, DeepSeek, and Grok 3 rely heavily on advanced chips for their massive computational needs, with companies even developing custom chips to optimize performance.

The Fundamental Differences: Semiconductors vs. Chips

What is a Semiconductor?

A semiconductor is a material with electrical conductivity between a conductor and an insulator at room temperature.

As its name suggests, it conducts electricity only partially, and its conductivity can be controlled within a specific range.

This unique property makes semiconductors incredibly valuable for modern electronics.

In semiconductors, the valence electrons (outer-shell electrons) are more loosely bound than insulators but not as free to move as in conductors.

The most common semiconductor material is silicon, although others, such as germanium and gallium arsenide, are also used.

The conductivity of semiconductors can be modified through “doping,” where small amounts of other elements, such as phosphorus or boron, are introduced to create either N-type or P-type semiconductors.

Semiconductors' ability to manipulate their electrical properties is fundamental to modern electronics.

What is a Chip?

A chip (a microchip or integrated circuit) is a small electronic device manufactured from semiconductor materials, primarily silicon.

It contains thousands to billions of miniaturized electronic components, such as transistors, resistors, capacitors, and diodes, etched onto a single semiconductor wafer.

The Collins Dictionary defines a silicon chip as “a tiny piece of silicon with electronic circuits on it which is part of a computer or other machinery.”

Chips are the physical implementation of complete electronic circuits on semiconductor materials, forming the building blocks of virtually all modern electronics.

Key Distinctions

The fundamental difference between the two can be summarized as

A semiconductor refers to the base material with specific electrical conductivity characteristics.

A chip (or microchip) is a manufactured device that etches electronic components onto a semiconductor wafer, forming a complete circuit.

Richard Electronics explains, “A Semiconductor refers to a material with specific electrical conductivity characteristics.

Microchip is the physical result of etching electronic components onto a semiconductor wafer, forming a complete circuit”.

Applications

Where Are Chips and Semiconductors Used?

Everyday Applications

Semiconductors and chips are ubiquitous in modern technology.

They form the foundation of virtually all electronic devices we use daily

Consumer Electronics

Smartphones, computers, televisions, gaming consoles, and digital cameras.

Transportation

Automotive systems, aircraft navigation, electric vehicles.

Medical Equipment

From simple monitoring devices to complex diagnostic machines.

Home AppliancesSmart refrigerators, washing machines, thermostats.

Industrial Equipment

Manufacturing systems, robotic controllers, and automation devices.

TechTarget notes, “Semiconductors underpin almost all modern technology, from simple clocks to advanced supersonic aircraft, all rely on semiconductors to function.”

Types of Chips and Their Uses

The main categories of semiconductor chips by functionality include:

Memory Chips

Store data and programs on computers and storage devices. Examples include RAM, ROM, and flash memory.

Microprocessors

Contain one or more central processing units (CPUs) for computers, servers, smartphones, and tablets.

Standard Chips

Perform specific functions like signal conversion or power management.

Complex Systems-on-a-Chip (SoCs)

Integrate multiple functions onto a single chip, often used in smartphones and embedded systems.

AI Applications and Their Reliance on Advanced Chips

OpenAI’s Hardware Strategy

OpenAI, the company behind ChatGPT, is making significant moves in the chip space

Custom Chip Development

OpenAI is finalizing the design of its first custom AI chip, which TSMC is expected to manufacture using advanced 3-nanometer process technology.

Reducing Nvidia Dependency

This chip development aims to reduce OpenAI’s reliance on Nvidia’s GPUs, which currently dominate the AI hardware market.

Dual-Purpose Design

The chip will be capable of training AI models and running inference (using trained models to generate responses).

Production Timeline

Mass production is slated for 2026, though the initial deployment will be modest.

Reuters says, “OpenAI is finalizing the design for its first custom AI training chip and is currently in the tape-out phase, the final process before a semiconductor is manufactured.”

DeepSeek’s Hardware Requirements

DeepSeek, another AI powerhouse, relies heavily on existing chip technology

Massive GPU Utilization

DeepSeek is estimated to use up to 50,000 GPUs for training and operating its AI model.

Enormous Memory Needs

Its 671B parameter model requires approximately 1.2TB of VRAM using FP16 precision or 400GB with 4-bit quantization.

Scalable Solutions

Smaller versions of DeepSeek models can run on high-end consumer GPUs, such as the NVIDIA RTX 4090 with 24GB of VRAM.

Grok 3’s Computational Requirements

Grok 3, xAI’s advanced language model, similarly depends on cutting-edge chip technology

Specialized Processing Units

Grok 3 leverages “optimized AI processing units,” including GPUs and TPUs, for accelerated training and inference.

Memory Architecture

It requires high-bandwidth memory (HBM) to ensure fast data retrieval and minimal latency.

Storage Solutions

NVMe SSDs provide quick access to large datasets and training checkpoints.

Why AI Needs Advanced Chips

A Simple Explanation

The Computational Challenge

Modern AI applications require enormous computational power for two main reasons:

Training

Processing vast amounts of data to learn patterns and relationships. This involves billions of mathematical calculations that must be performed in parallel.

Inference

Running the trained models to generate responses or predictions, which still requires significant computational resources, especially for large models.

Why Specialized Chips?

Regular computer processors (CPUs) aren’t optimized for the specific type of parallel processing that AI requires.

Specialized chips like GPUs and custom AI accelerators are designed specifically to

Process in Parallel

Perform thousands of calculations simultaneously rather than sequentially.

Optimize for AI Operations

Handle the specific types of math (like matrix multiplications) that dominate AI workloads.

Provide Memory Bandwidth

Deliver the massive amounts of data needed for AI computations quickly.

The Custom Chip Trend

The trend toward custom chips in companies like OpenAI highlights the specific needs of AI that even general-purpose GPUs can’t fully address:

Efficiency

Custom chips can be optimized for specific AI models, reducing power consumption and cost.

Performance

Tailored designs can accelerate specific operations used frequently in particular AI systems.

Independence

Developing custom chips reduces dependence on suppliers like Nvidia, whose GPUs are in high demand and often face supply constraints.

Conclusion

The distinction between semiconductors and chips is fundamental to understanding modern electronics.

Semiconductors are the special materials with unique electrical properties, while chips are the manufactured devices that leverage these properties to create functional electronic systems.

In the AI world, advanced chips built from semiconductor materials form the backbone of computational infrastructure.

As AI systems grow more complex, the demand for specialized chips increases, leading companies like OpenAI to develop custom solutions tailored to their specific needs.

This evolution in hardware parallels the rapid advancement in AI capabilities, highlighting the crucialQ relationship between semiconductor technology and artificial intelligence development.

For everyday users, this means that the continued advancement of AI applications depends not just on software innovations but also on breakthroughs in the physical chips that power these systems.