CPU, GPU, TPU, NPU Explained: Choosing the Right AI Hardware | Muskan

CPU, GPU, TPU, NPU: the video calls it alphabet soup, but says one simple idea sits underneath it. AI does mostly one kind of math, and these chips are different bets on how to do that math fast. Muskan offers a lens that makes all four make sense, and ends on a warning against treating the list as a ranking.

Start with the workload

Normal computing is a few complex steps done in order, which is what a CPU is great at. AI is different. It is billions of tiny multiply-and-add operations, the same simple math over and over. To be fast at that, the video says you do not want one clever core; you want to do enormous amounts of it at once. The summary line is that AI is parallel matrix math.

The four chips

Against that workload, the four chips line up like this:

• A CPU has a few powerful cores and runs everything.
• A GPU has thousands of simpler cores built for parallel math, and it is the workhorse of AI.
• A TPU is a custom chip built specifically for tensor math.
• An NPU is a small, low-power chip that runs AI right on your device.

A spectrum from general to specialized

Placed in a line, the chips form a spectrum from general to specialized. The CPU does anything. The GPU is faster at AI but less flexible. The TPU and NPU are faster still, but they only do a narrow job. The rule the video gives: the more specialized the chip, the more AI speed you get, and the less it can do anything else.

Example: unlocking your phone with your face

Face unlock is a small AI job that has to be instant, private, and work with no signal. So it does not go to a giant GPU in the cloud. It runs on the NPU inside the phone, in milliseconds. Small job, on device, low power: that is the NPU's whole reason to exist.

The leaderboard trap

The video warns that this is not a ranking, and newer does not mean better. A TPU cannot run your operating system. An NPU will not train a big model. A CPU still runs the whole show and hands the heavy math off. It is a division of labor, not a leaderboard, and you match the chip to the job.

Key takeaways

• AI is mostly parallel matrix math: billions of simple multiply-and-add steps.
• A CPU has a few powerful cores and handles general work.
• A GPU has thousands of simpler cores and is the workhorse of AI.
• A TPU is custom-built for tensor math.
• An NPU is a small, low-power chip for AI on the device itself.
• More specialization means more AI speed but less flexibility.
• Face unlock runs on the NPU because it must be instant, private, and work offline.
• The chips are a division of labor, not a leaderboard.

Try it yourself

Look at the device you are using right now and ask whether it has an NPU or another AI chip. Then list which of its AI features run on the device and which call the cloud, and ask why each one lives where it does. The video describes that question as the whole lesson applied to your own hardware.