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What is an AI chip and how does it differ from a CPU and GPU? – Understanding artificial intelligence hardware

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The revolution of artificial intelligence (AI) is not only a software-driven phenomenon—it is fundamentally reshaping the world of hardware as well. While most users interact with AI through apps, chatbots, or image generators, all of this is made possible by powerful underlying hardware specifically designed for efficient neural network and machine learning (ML) processing. These hardware components are known as AI chips, and they represent a new category that complements traditional CPUs (central processing units) and GPUs (graphics processing units).

This article offers a comprehensive overview of what AI chips are, how they differ from existing processors, the various types, and the practical applications of these chips in devices ranging from smartphones to data centers. We’ll also look ahead to future trends and explore what this means for developers and end users alike.

Table of contents

  1. What is an AI chip?

  2. Why aren’t CPUs and GPUs enough for AI?

  3. Overview of AI chip categories

  4. Types of AI chips and examples

  5. How AI chips are optimized for artificial intelligence

  6. AI chips in mobile devices

  7. AI chips in servers and cloud computing

  8. Local vs. cloud-based AI processing

  9. AI chip considerations: power, heat, security

  10. Leading AI chip platforms by major tech companies

  11. Future trends: edge AI, open hardware, quantum computing

  12. Summary

1. What is an AI chip?

An AI chip is a specialized processor designed to accelerate machine learning (ML) and deep learning (DL) tasks. These chips are optimized for the matrix calculations and pattern recognition required to train and execute neural networks.

Key characteristics:

  • Massive parallel processing

  • Dedicated processing units for multiply-accumulate operations (MACs)

  • Low latency

  • High memory bandwidth

AI chips can outperform traditional processors in both speed and energy efficiency for AI-specific tasks.

2. Why aren’t CPUs and GPUs enough for AI?

CPUs (Central Processing Units)

  • General-purpose processors

  • Excellent at sequential tasks and control logic

  • Limitation for AI: not efficient for large-scale matrix computation

GPUs (Graphics Processing Units)

  • Thousands of parallel threads, high throughput

  • Originally designed for graphics, now widely used in AI

  • Limitation: high power consumption, not purpose-built for all AI operations

AI chips are custom-built for AI workflows, enabling greater efficiency and performance compared to CPUs or GPUs.

3. Overview of AI chip categories

Type Description Key companies
TPU (Tensor Processing Unit) Google’s custom AI chip, mostly for cloud AI Google
NPU (Neural Processing Unit) AI accelerators in mobile devices Apple, Huawei, Samsung
VPU (Vision Processing Unit) Optimized for video/image AI Intel, Movidius
ASIC (Application-Specific Integrated Circuit) Purpose-built AI hardware Tesla, Groq
FPGA (Field Programmable Gate Array) Programmable for AI acceleration Xilinx, Intel

4. Types of AI chips and examples

TPU – Tensor Processing Unit

  • Google’s AI chip for services like Gmail, YouTube, Bard

  • Exceptional performance-per-watt and performance-per-dollar

  • Optimized for TensorFlow models

NPU – Neural Processing Unit

  • Designed for smartphones and tablets

  • Examples: Apple Neural Engine, Huawei Da Vinci architecture

  • Used for: facial recognition, translation, voice assistants

VPU – Vision Processing Unit

  • Handles real-time image and video AI workloads

  • Used in Intel Movidius chips for smart cameras, laptops, AR glasses

FPGA and ASIC

  • FPGAs are reprogrammable and good for AI prototyping

  • ASICs are hardwired for maximum AI efficiency (e.g., Tesla Dojo)

5. How AI chips are optimized for artificial intelligence

AI workloads rely heavily on matrix multiplications and additions, the foundation of neural network training and inference.

Core optimizations:

  • Tens of thousands of simultaneous operations

  • Custom memory hierarchy (caches, buffers)

  • Precision formats like FP16, INT8 for faster, low-power execution

AI chips can be 10–50× more efficient than CPUs or GPUs for AI tasks.

6. AI chips in mobile devices

Modern smartphones increasingly feature built-in NPUs for on-device intelligence.

Examples of mobile AI features:

  • Facial unlock (e.g., Face ID)

  • Real-time camera enhancements (night mode, depth effects)

  • Offline voice processing (e.g., Siri, Bixby)

  • On-device translation

Leading chips:

  • Apple Neural Engine (up to 35 TOPS)

  • Samsung Exynos AI Engine

  • Qualcomm Hexagon DSP with AI acceleration

7. AI chips in servers and cloud computing

Training and deploying large AI models like ChatGPT or Gemini requires immense computational resources.

Key hardware:

  • Google TPU v4, v5 – used in Google Cloud

  • NVIDIA A100, H100 – backbone of OpenAI, Meta, Microsoft infrastructure

  • Amazon Inferentia, Trainium – Amazon’s custom AI chips

These chips are the powerhouses behind cutting-edge generative AI and deep learning.

8. Local vs. cloud-based AI processing

Criteria Local AI (NPU) Cloud AI (TPU, GPU)
Latency Very low Higher
Privacy High Lower
Performance Limited Scalable
Offline support Yes No
Use cases Cameras, assistants LLMs, image generation, analytics

Local AI is ideal for speed and privacy, while cloud AI handles high-volume, high-complexity workloads.

9. AI chip considerations: power, heat, security

High-performance AI chips, like NVIDIA’s H100, can exceed 700W, requiring:

  • Liquid cooling systems

  • Advanced heat spreaders

  • Efficient passive cooling for mobile NPUs

Security and efficiency:

  • Power efficiency is crucial for edge AI (IoT, robotics)

  • Secure enclaves and isolated processing zones protect sensitive AI data

10. Leading AI chip platforms by major tech companies

Company Chip Primary Use
Google TPU Cloud AI infrastructure
Apple Neural Engine On-device AI (iPhone, iPad, Mac)
NVIDIA A100, H100 AI training and inference
Amazon Inferentia, Trainium AWS AI workloads
Intel Gaudi, Habana AI acceleration in enterprise
Tesla Dojo Self-driving neural networks

11. Future trends: edge AI, open hardware, quantum computing

Edge AI

  • AI runs directly on the device (no cloud)

  • Key for IoT, smart homes, autonomous vehicles

Open hardware

  • RISC-V-based AI chips gaining traction

  • More open-source frameworks (e.g., OpenVINO, ONNX)

Quantum AI

  • Still experimental, but may enable nonlinear, massive-scale models in the long term

12. Summary

AI chips are purpose-built processors designed to supercharge artificial intelligence. While CPUs and GPUs still have a place, the future of AI processing will rely more heavily on these specialized, efficient, and scalable chips.

From smartphones and cameras to massive data centers and autonomous vehicles, AI chips are the invisible engines behind the intelligent systems shaping our future.



Image(s) used in this article are either AI-generated or sourced from royalty-free platforms like Pixabay or Pexels.

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