Graphics Processing Units (GPUs)
GPUs are the most widely used chips for artificial intelligence today. Originally designed for video games and graphics, GPUs are excellent at AI because they can perform many calculations at the same time. NVIDIA is the leading company making GPUs for AI work. GPUs like the A100 and H100 are popular in data centers where AI models are trained and used.
Tensor Processing Units (TPUs)
TPUs are specialized chips created by Google specifically for artificial intelligence tasks. These chips are designed to handle a type of math calculation called tensor operations, which is the foundation of most AI models. TPUs are very fast at AI work but are primarily used within Google's systems and services. They represent the trend of companies building custom chips optimized for their AI needs.
Central Processing Units (CPUs)
CPUs are the general-purpose processors found in computers and servers. While not specialized for AI, CPUs can run AI models and are useful for smaller AI tasks or when specialized chips are not available. Major companies like Intel and AMD make CPUs that support AI computing. CPUs are slower than GPUs and TPUs for large AI jobs but offer more flexibility.
Other Specialized AI Chips
Besides GPUs and TPUs, other companies are developing custom AI chips. Intel makes Gaudi processors, Apple creates Neural Engines for its devices, and many startups are designing chips optimized for specific AI applications. These specialized chips aim to make AI faster, cheaper, or more energy-efficient for particular uses.
Why AI Needs Special Chips
Artificial intelligence requires processing huge amounts of data and performing billions of calculations. Regular computer chips were not designed for this type of work. Specialized AI chips can do many mathematical operations at the same time, making them thousands of times faster than traditional chips for AI tasks. This speed is essential for training large AI models and running them quickly. 
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