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AI Chip

Last reviewed: April 2026

A processor designed or optimised to efficiently run AI and machine learning workloads, including GPUs, TPUs, and purpose-built neural processors.

An AI chip is any processor specifically designed or optimised to run artificial intelligence workloads efficiently. This includes GPUs, TPUs, NPUs, and entirely custom processors built from the ground up for machine learning tasks.

Why AI needs special chips

Traditional CPUs are general-purpose — they can run any software but are not optimised for any specific task. AI workloads involve massive matrix multiplications and parallel operations that benefit enormously from specialised hardware. A purpose-built AI chip can be 10-100 times more efficient than a CPU for these operations.

The key players

  • NVIDIA: Dominates the AI chip market with GPUs (A100, H100, B200). Their CUDA software ecosystem is deeply embedded in AI research and industry.
  • Google: Builds TPUs for internal use and cloud customers. Powers Google's own AI services including Gemini.
  • AMD: Challenger to NVIDIA with MI300 series GPUs, gaining traction on price-performance ratio.
  • Apple: Integrates Neural Engines into M-series chips for on-device AI in iPhones, iPads, and Macs.
  • Intel: Developing Gaudi accelerators for data centre AI workloads.
  • Startups: Companies like Groq, Cerebras, and SambaNova are building novel architectures optimised for specific AI workloads like fast inference.

AI chips in everyday devices

AI chips are increasingly embedded in consumer devices:

  • Smartphones: Enable real-time photo enhancement, voice recognition, and on-device language models
  • Laptops: Power features like background blur in video calls and smart text suggestions
  • Cars: Process camera and sensor data for driver assistance features
  • Smart speakers: Handle wake-word detection and basic voice processing locally

The strategic dimension

AI chips have become a geopolitical concern. The US has imposed export controls restricting the sale of advanced AI chips to certain countries. Nations are investing heavily in domestic AI chip development. NVIDIA's market capitalisation surpassed $3 trillion, reflecting the strategic importance of AI compute hardware.

What this means for businesses

For most businesses, the AI chip landscape matters indirectly — through the cost and availability of cloud AI services. When NVIDIA GPUs are scarce, cloud computing prices rise. When new, more efficient chips arrive, AI costs decrease. Understanding the chip market helps you anticipate cost trends and evaluate vendor offerings.

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Why This Matters

AI chips are the physical foundation of all AI capabilities. While most businesses do not buy AI chips directly, understanding the landscape helps you anticipate compute cost trends, evaluate cloud providers, and understand why AI capabilities are advancing so rapidly — and why compute access has become a strategic concern.

Related Terms

AI Accelerator
Specialised hardware designed to perform AI computations faster and more efficiently than general-purpose processors.
GPU (Graphics Processing Unit)
A specialised processor originally designed for rendering graphics but now essential for training and running AI models. GPUs can perform thousands of calculations simultaneously.
TPU (Tensor Processing Unit)
A custom AI chip designed by Google specifically for machine learning workloads. TPUs are optimised for the tensor operations that neural networks rely on.
Inference
The process of an AI model generating output from your input. Every time you send a prompt and get a response, that is inference.
Quantization
A technique that reduces the precision of an AI model's numerical weights to make it smaller, faster, and cheaper to run.
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This topic is covered in our lesson: AI Infrastructure and Deployment

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