An AI chip is a processor designed or well suited to perform the huge numbers of calculations that artificial intelligence needs. The best known are GPUs (graphics processing units), originally built for video games, which have become the main engines for training and running AI models.
Why AI needs special chips
AI models work by performing enormous numbers of simple mathematical operations, mostly multiplying large tables of numbers called matrices. A regular CPU (central processing unit) is designed to handle many different tasks one after another, very flexibly. A GPU contains thousands of smaller cores that perform many calculations at the same time, which makes it far faster for AI workloads.
Types of AI chips
GPUs are the most widely used AI chips, valued for performance and a mature software ecosystem. Nvidia has been the dominant supplier of AI GPUs, with AMD among competitors. Custom AI accelerators, sometimes called ASICs, are designed specifically for AI; large cloud companies build their own, such as Google's TPUs. NPUs (neural processing units) are smaller AI engines built into phones and laptops to run AI features on the device. FPGAs are reprogrammable chips used for some specialised tasks.
Training versus inference
Training means teaching a model by processing vast datasets, which requires huge clusters of powerful chips running for weeks or months. Inference means using a trained model to answer requests, which happens billions of times a day. As AI use grows, demand for efficient inference chips is rising rapidly.
Memory matters
AI chips need to move data very fast. High-bandwidth memory (HBM), stacked memory placed close to the processor, has become a critical and often scarce component. A small number of memory makers produce it, and supply has at times limited how many AI chips could be built.
How chips are made
Advanced chips are designed by companies such as Nvidia and AMD but manufactured by specialised foundries, with TSMC in Taiwan producing most of the world's most advanced chips. Manufacturing needs extremely complex equipment, and the supply chain spans many countries. This concentration has made semiconductors a matter of national strategy, with governments investing in domestic chip production and imposing export controls on advanced AI chips.
India and semiconductors
India launched the India Semiconductor Mission in 2021 to build a chip ecosystem, offering incentives for chip fabrication, assembly, testing and packaging plants. Several projects have been approved, including a fabrication plant and multiple assembly and testing facilities. India also has a large chip design workforce, with many global companies running design centres in the country.
Why it matters for investors
AI chips are at the heart of the AI investment boom. Demand affects chip designers, manufacturers, memory makers, equipment suppliers and data centers. The industry is also cyclical, highly competitive and affected by geopolitics, so it carries significant risk alongside growth.
Common misconceptions
"GPUs are only for gaming." They are now central to AI.
"Chip designers make their own chips." Many outsource manufacturing to foundries.
"Only one company makes AI chips." Several companies compete, including custom chip makers.
Frequently asked questions
What is a GPU?
A processor with many cores that perform calculations in parallel, ideal for AI.
What is HBM?
High-bandwidth memory, stacked close to AI chips for fast data access.
What is the difference between training and inference?
Training builds the model; inference uses it to answer requests.
Is India making chips?
India is building fabrication and assembly facilities under the India Semiconductor Mission.
How PositoAI fits in
PositoAI covers companies in India, the US and other markets. Research technology and electronics companies on their PositoAI pages, including red flags and financial history. Read our guide to data centers.
Summary
AI chips perform the massive parallel calculations AI requires. GPUs dominate, alongside custom accelerators, NPUs and FPGAs. Training needs huge chip clusters; inference demand is growing fast. High-bandwidth memory is critical. Manufacturing is concentrated in a few foundries, making chips strategic. India is building capacity through the India Semiconductor Mission.