The IT world has been deeply immersed in the AI revolution over the past two years. Terms like GenAI, accelerators, diffusion, and inference are now common, and the understanding that GPUs are valuable beyond video games is well-established. However, certain specialized topics within AI and ML, such as the TPU, remain less understood. What, after all, does thermoplastic polyurethane have to do with Artificial Intelligence? (Just kidding 😉) In the realm of AI and computing, TPU stands for Tensor Processing Unit. This series of articles aims to address and clarify popular myths and misconceptions surrounding this highly specialized technology.
Myth 1: A TPU is just Google’s brand name for a GPU
It is easy to understand where this misconception comes from. The TPU and GPU are often referred to as the engines of Artificial Intelligence. So, if it walks like a duck, it quacks like a duck… it’s a duck, right? Not in this case. TPUs and GPUs do serve a similar purpose in this case, however they are far from being the same. The GPUs are far more versatile in terms of what they can compute. After all, they are also used for processing graphics, rendering 3D models and so on. Have you ever heard someone mention a TPU in this context? A simple venn diagram can help here, it will show the range of tasks a specific chip can handle:
It all comes down to the purpose of the different architectures in those chips.
- Central Processing Unit (CPU): This is a general-purpose processor, designed with a few powerful cores to handle a diverse range of tasks sequentially and quickly, from running an operating system to a word processor.
- Graphics Processing Unit (GPU): This is a specialized processor originally designed for the highly parallel task of rendering graphics. Researchers later discovered that this parallel architecture — thousands of simpler cores — was highly effective for the parallel mathematics of AI. The GPU was adapted or co-opted for AI, evolving into a GPGPU, a general-purpose parallel computer.
- Tensor Processing Unit (TPU): This is an ASIC (Application-Specific Integrated Circuit). It was not adapted from another purpose; it was architected from the ground up for one specific application: accelerating neural network operations. Its silicon is dedicated only to the massive matrix and tensor operations fundamental to AI. It is, by design, an inflexible chip; it can’t run word processors or render graphics.
This architectural difference highlights why directly comparing GPU and TPU performance is often problematic. It’s challenging to compare devices not designed for identical tasks — perhaps less like comparing apples to oranges, and more like comparing apples to pears, each optimized for different purposes.
Myth 2: TPUs are always cheaper/TPUs are always more expensive than GPU
The comparison of TPU pricing versus GPU pricing is a popular point of confusion. Determining which offers superior cost-effectiveness — which one “gives you more bang for the buck” — is far from a straightforward answer.
While numerous claims suggest TPUs are significantly cheaper than various GPUs, these assertions invariably come with caveats: they often apply only to specific models, certain tasks, or particular configurations. The reality is, there’s no simple formula to determine how one TPU compares in cost-effectiveness to another accelerator.
To find out the real performance of a TPU system, you will need to run experiments. This also applies to GPU systems — the whole system depends on much more than just accelerator performance, that’s why it’s important to compare very specific scenarios, including the storage, networking and the type of workload you want to run.
More to come
These were the first two common myths about TPUs. I hope this explanation has provided some clarity, even if the answers aren’t always straightforward. In the next article, I will delve deeper into TPU costs, as the topic extends beyond a simple ‘it depends.’ To stay updated on the latest TPU news and other exciting announcements, be sure to follow the official Google Cloud blog and the GCP YouTube channel!
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