AI Hardware: GPUs, TPUs, and NPUs Explained
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cdteliot
3 min read
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As artificial intelligence continues to advance, specialized hardware like GPUs, TPUs, and NPUs are becoming increasingly crucial for accelerating AI workloads and improving efficiency. These purpose-built processors offer unique advantages over traditional CPUs in handling the complex mathematical operations required for machine learning and deep learning tasks.
What’s the Big Deal About AI Hardware?
Let's be honest, when most people hear "AI," they think of chatGPT, complex machine learning models, and maybe even the robots from sci-fi movies. The reality behind AI, however, looks much more like data centers full of GPUs running calculations
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. You can think of AI a bit like cooking—sure, you can make a meal with a regular oven, but a high-end convection oven is going to get the job done way faster and better. In AI, the "oven" comes in the form of specialized hardware: GPUs, TPUs, and NPUs3
. Because yes, it's actually the hardware that makes AI as quick and efficient as it is today4
. So what exactly do those chips do, and how do they fit into the world of AI? Let's dive into each of these, one at a time.4 sources
GPUs: The AI Workhorse
amax.com
If you've ever built a gaming computer or know someone who has, you've probably heard of Graphics Processing Units or GPUs for short. GPUs are chips built to handle parallel processing, meaning they can perform thousands of calculations at the same time, allowing them to go much faster than regular chips
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. A few years back, these chips were mainly used by gamers who needed powerful hardware that could render images and videos properly, making sure their computer wouldn't lag when playing video games1
. But, turns out, the same characteristics that make GPUs great for games also make them great for AI, and GPUs have quickly become the must-have technology everyone is competing for3
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TPUs: The Speed Specialists
extremetech.com
Tensor Processing Units (TPUs) are Google's custom-made AI chips. And if GPUs can handle a bit of everything, TPUs on the other hand were built for speed specifically. To be more precise, TPUs are optimized for tensor operations, which are fundamental to many machine learning models, especially deep learning
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. You can see it this way: if GPUs are multitaskers able to handle several things at once, TPUs would be more like specialists you would call when you need something done quickly and efficiently. So if TPUs are not as good as GPUs when it comes to general-purpose computing, when it comes to training and running machine learning models—particularly in Google's cloud infrastructure—TPUs are a game-changer2
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NPUs: The Future of AI on Your Phone
qualcomm.com
Neural Processing Units (NPUs), finally, are slowly becoming a big deal—especially in mobile devices. Actually, you may not know it, but Apple, Google, and Huawei are all integrating NPUs into their phones to handle AI tasks without draining your phone's battery.
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See when you're taking a portrait of your friend with your phone and it magically blurs the background? Yep, that's the NPU doing its thing.3
The real magic of NPUs is that they can do all this, handling extremely complex tasks like image recognition, voice processing, or even augmented reality without needing tons of power and draining the phone of its battery.4
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Why Do We Need All These Different Chips?
backblaze.com
As we've just seen together, the reason we have different hardware like GPUs, TPUs, and NPUs is that each one is optimized for very different tasks. If we had to summarize: GPUs are versatile chips that can do a bit of everything, which is why they're still used in a lot of AI tasks.
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TPUs are hyper-focused on certain types of AI tasks, making them faster at specific tasks.3
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NPUs are specialized for mobile, designed to do AI things fast, without using much power.5
Each of these chips plays a unique role in making AI faster, more efficient, and more accessible. Depending on the task—whether it's training massive AI models in a data center or running real-time AI apps on your phone—there's hardware built specifically for the job.6
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Closing Thoughts on AI Hardware
As seen in this article, GPUs, TPUs, and NPUs are the backbone of what we call AI today. Each has its place, and together, they're pushing the boundaries of what AI can do—whether it's recognizing images, translating languages, or powering self-driving cars
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. As AI keeps pushing the limits of what's possible, you can bet the hardware will keep evolving right alongside it. We're probably going to see even more specialized chips designed to handle specific AI tasks, making everything faster, more efficient, and less power-hungry3
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. So the next time you're watching a Netflix recommendation pop up or using face unlock on your phone, remember: these tasks don't rely solely on software, there's also some hardware magic happening behind the scenes with GPUs, TPUs, or NPUs doing their part to make AI as powerful—and as fast—as it is today5
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. Pretty cool, huh?6 sources
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